Different classes of antibacterial and antifungal drugs and their mechanism of action.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"6058",leadTitle:null,fullTitle:"Recent Advances and Applications in Remote Sensing",title:"Recent Advances and Applications in Remote Sensing",subtitle:null,reviewType:"peer-reviewed",abstract:"Remote sensing was the primary data source since the launch of the first environmental monitoring satellite back in 1972. In the past five decades, remote sensing technology has come a long way and evolved into a mature science. Even so, new technologies, new theories, new methodologies, and new applications continue to emerge. With the rapid pace of technological advancement, it is essential to share experiences especially between different disciplines, either on breakthroughs in new theory or understanding, or applications of remote sensing on real world issues. Disciplines or fields covered in this book include geography, geology, agriculture, forestry, botany, and oceanography. Though remote sensing may be used differently in various disciplines, the principles are similar, if not the same. This book will be valuable to scientists, scholars, working professionals, or students who use remote sensing in their work, and are interested in learning how others use remote sensing in different ways.",isbn:"978-1-78923-537-1",printIsbn:"978-1-78923-536-4",pdfIsbn:"978-1-83881-271-3",doi:"10.5772/67959",price:119,priceEur:129,priceUsd:155,slug:"recent-advances-and-applications-in-remote-sensing",numberOfPages:214,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"6c271ba0275d66a79d494e5e0bfe9ebb",bookSignature:"Ming-Chih Hung and Yi-Hwa Wu",publishedDate:"July 25th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6058.jpg",numberOfDownloads:13145,numberOfWosCitations:12,numberOfCrossrefCitations:14,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:23,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:49,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 22nd 2017",dateEndSecondStepPublish:"March 15th 2017",dateEndThirdStepPublish:"July 15th 2017",dateEndFourthStepPublish:"October 14th 2017",dateEndFifthStepPublish:"December 20th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"184413",title:"Dr.",name:"Ming",middleName:"Chih",surname:"Hung",slug:"ming-hung",fullName:"Ming Hung",profilePictureURL:"https://mts.intechopen.com/storage/users/184413/images/4908_n.jpg",biography:"Ming-Chih Hung is a Professor of Geography/GIScience at Northwest Missouri State University. He earned his bachelor degree from National Taiwan University focusing on DEM applications, his MS from the University of Utah focusing on the V-I-S Model and soft classification of TM images, and his Ph.D. also from the University of Utah focusing on remote sensing and GIS on urban areas. His research emphasizes on use of GIScience on urban areas. In addition to urban areas, he is also interested in precision agriculture and environmental issues. At Northwest Missouri State, he teaches courses on remote sensing, GIS, GPS, and cartography.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Northwest Missouri State University",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"181853",title:"Dr.",name:"Yi-Hwa",middleName:null,surname:"Wu",slug:"yi-hwa-wu",fullName:"Yi-Hwa Wu",profilePictureURL:"https://mts.intechopen.com/storage/users/181853/images/7664_n.jpg",biography:"Dr. Yi-Hwa Wu is a Professor of Geography/GIScience at Northwest Missouri State University. She earned her bachelor’s degree and MS from National Taiwan University, both focusing on the geographic information system (GIS) modeling process. She earned her Ph.D. from the University of Utah focusing on modeling the dynamic nature of urban transportation systems. Her research emphasizes the GIS database and design, GIS models, and modeling processes in various environments, such as transportation models, predictive models, etc. At Northwest Missouri State University, she teaches courses on GIS, cartography, and GIS programming.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Northwest Missouri State University",institutionURL:null,country:{name:"United States of America"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"653",title:"Remote Sensing",slug:"geology-and-geophysics-remote-sensing"}],chapters:[{id:"58904",title:"A Study on Phenology Detection of Corn in Northeastern China with Fused Remote Sensing Data",doi:"10.5772/intechopen.73096",slug:"a-study-on-phenology-detection-of-corn-in-northeastern-china-with-fused-remote-sensing-data",totalDownloads:1100,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Accurate phenology information detection is the basis for other remote-sensing based agriculture applications. So far, there have been a lot of phenology estimation models based on remote-sensing data, but little attention was paid to microscopic mechanism of crops and the environmental factors. The main purpose of this chapter is to apply a new phenology detection model, which combined physical mechanism-based crop models with remote-sensing data to detect the critical phenological stages of corn in Northeast China (Jilin and Liaoning Provinces). Compared to the phenology observations from the agriculture meteorological stations, the corn phenology estimation accuracy in Northeast China using only MODIS data is much lower than that in the US field sites. The main reason might be the small size of single piece of cropland in northeastern China, which led to the mixed MODIS pixels. Accordingly, Landsat and MODIS data fusion methods were applied to get time-series images with Landsat-like spatial resolution and MODIS-like temporal resolution, and quantitative and qualitative validation was conducted to evaluate and verify the accuracy of the data fusion. The results show that data fusion of Landsat and MODIS improved the spatial resolution and decreased the influence of mixed pixels.",signatures:"Linglin Zeng and Daxiang Xiang",downloadPdfUrl:"/chapter/pdf-download/58904",previewPdfUrl:"/chapter/pdf-preview/58904",authors:[{id:"119463",title:"Dr.",name:"Daxiang",surname:"Xiang",slug:"daxiang-xiang",fullName:"Daxiang Xiang"},{id:"206943",title:"Dr.",name:"Linglin",surname:"Zeng",slug:"linglin-zeng",fullName:"Linglin Zeng"}],corrections:null},{id:"58331",title:"Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification",doi:"10.5772/intechopen.72577",slug:"despeckling-of-multitemporal-sentinel-sar-images-and-its-impact-on-agricultural-area-classification",totalDownloads:1277,totalCrossrefCites:6,totalDimensionsCites:9,hasAltmetrics:0,abstract:"This chapter addresses an important practical task of classification of multichannel remote sensing data with application to multitemporal dual-polarization Sentinel radar images acquired for agricultural regions in Ukraine. We first consider characteristics of dual-polarization Sentinel radar images and discuss what kind of filters can be applied to such data. Several examples of denoising are presented with analysis of what properties of filters are desired and what can be provided in practice. It is also demonstrated that the use of preliminary denoising produces improvement of classification accuracy where despeckling that is more efficient in terms of standard filtering criteria results in better classification.",signatures:"Vladimir Lukin, Oleksii Rubel, Ruslan Kozhemiakin, Sergey Abramov,\nAndrii Shelestov, Mykola Lavreniuk, Mykola Meretsky, Benoit Vozel\nand Kacem Chehdi",downloadPdfUrl:"/chapter/pdf-download/58331",previewPdfUrl:"/chapter/pdf-preview/58331",authors:[{id:"58658",title:"Prof.",name:"Vladimir",surname:"Lukin",slug:"vladimir-lukin",fullName:"Vladimir Lukin"},{id:"58660",title:"Dr.",name:"Benoit",surname:"Vozel",slug:"benoit-vozel",fullName:"Benoit Vozel"},{id:"58661",title:"Prof.",name:"Kacem",surname:"Chehdi",slug:"kacem-chehdi",fullName:"Kacem Chehdi"},{id:"213471",title:"Dr.",name:"Oleksii",surname:"Rubel",slug:"oleksii-rubel",fullName:"Oleksii Rubel"},{id:"213476",title:"Dr.",name:"Ruslan",surname:"Kozhemiakin",slug:"ruslan-kozhemiakin",fullName:"Ruslan Kozhemiakin"},{id:"213477",title:"Dr.",name:"Sergey",surname:"Abramov",slug:"sergey-abramov",fullName:"Sergey Abramov"},{id:"213482",title:"Prof.",name:"Andrii",surname:"Shelestov",slug:"andrii-shelestov",fullName:"Andrii Shelestov"},{id:"213484",title:"Dr.",name:"Mykola",surname:"Lavreniuk",slug:"mykola-lavreniuk",fullName:"Mykola Lavreniuk"},{id:"213488",title:"MSc.",name:"Mykola",surname:"Meretsky",slug:"mykola-meretsky",fullName:"Mykola Meretsky"}],corrections:null},{id:"57650",title:"Remote Sensing of Forests using Discrete Return Airborne LiDAR",doi:"10.5772/intechopen.71777",slug:"remote-sensing-of-forests-using-discrete-return-airborne-lidar",totalDownloads:1010,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests, however, remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level. When applied to the University of Kentucky Robinson Forest, the segmentation method detected about 90% of overstory and 47% of understory trees with over-segmentation rates of 14 and 2%. Stratifying the canopy improved the detection rate of understory trees to 68% at the cost of increasing their over-segmentations to 16%. According to our occlusion model, a point density of ~170 pt/m2 is needed to segment understory trees as accurately as overstory trees. Lastly, using the distributed approach, we segmented about two million trees in the 7440-ha forest in 2.5 hours using 192 processors, which is 167 times faster than using a single processor.",signatures:"Hamid Hamraz and Marco A. Contreras",downloadPdfUrl:"/chapter/pdf-download/57650",previewPdfUrl:"/chapter/pdf-preview/57650",authors:[{id:"207206",title:"Ph.D. Student",name:"Hamid",surname:"Hamraz",slug:"hamid-hamraz",fullName:"Hamid Hamraz"},{id:"207292",title:"Prof.",name:"Marco",surname:"Contreras",slug:"marco-contreras",fullName:"Marco Contreras"}],corrections:null},{id:"59160",title:"Remote Sensing in Land Applications by Using GNSS-Reflectometry",doi:"10.5772/intechopen.72901",slug:"remote-sensing-in-land-applications-by-using-gnss-reflectometry",totalDownloads:1897,totalCrossrefCites:5,totalDimensionsCites:7,hasAltmetrics:0,abstract:"Global navigation satellite system-reflectometry (GNSS-R) as an efficient tool for remote sensing has gained increasing interests in the last two decades, due to its unique characteristics. It uses GNSS signals as sources of opportunity, providing precise, continuous, all-weather, and 24 hours’ detections, which play a key role in many land applications. The fundamental theoretical part of GNSS-R technique is examined at first. Then, GNSS-R methodologies applied in the soil moisture content, vegetation biomass sensing, and altimetry applications are also detailed. One retrieval method uses only RH (right-hand) reflected data. Another retrieval method for soil moisture content (SMC) aimed to calibrate the measurement by using water reflections, based on the bistatic equations with LH (left-hand) reflected and RH direct signals. The other method for SMC retrieval is related to the polarimetric ratio (PR), the ratio of LH/RH reflected signals can reveal the fluctuations of the SMC. Another vital parameter vegetation biomass was observed by using the variation of reflectivity of the LH and RH reflected components. Finally, the C/A code method was used for exploring the possibility to the altimetry estimation. The features of GNSS-R technique made it a promising remote sensing technique in hydrology, climatology carbon cycles, and other potential applications.",signatures:"Yan Jia and Yuekun Pei",downloadPdfUrl:"/chapter/pdf-download/59160",previewPdfUrl:"/chapter/pdf-preview/59160",authors:[{id:"210019",title:"Dr.",name:"Yan",surname:"Jia",slug:"yan-jia",fullName:"Yan Jia"},{id:"217368",title:"Dr.",name:"Yuekun",surname:"Pei",slug:"yuekun-pei",fullName:"Yuekun Pei"}],corrections:null},{id:"58777",title:"Remote Sensing with Shipborne High-Frequency Surface-Wave Radar",doi:"10.5772/intechopen.72833",slug:"remote-sensing-with-shipborne-high-frequency-surface-wave-radar",totalDownloads:946,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"High-frequency surface-wave radar (HFSWR) has been successfully applied for moving target detection and remote sensing of ocean surface dynamic parameters for decades. Compared with conventional instruments such as buoys, anemometers, and microwave radars, HFSWR can be employed to an all-weather and all-time surveillance far beyond the visible horizon. Moreover, based on agility and maneuverability, shipborne HFSWR can not only enhance the survivability in complex ocean environment but also enlarge the detection distance on open sea, which will gradually become a popular deployment situation. In this chapter, ocean surface cross sections for shipborne HFSWR with linear platform motion and sway motion are derived theoretically. Then, the methods for ocean surface wind direction, wind field, and current extraction are presented. The computer simulations and experimental results of the real data are given to verify the detection accuracy and the distance limit of the abovementioned methods.",signatures:"Junhao Xie, Minglei Sun, Zhenyuan Ji and Guowei Yao",downloadPdfUrl:"/chapter/pdf-download/58777",previewPdfUrl:"/chapter/pdf-preview/58777",authors:[{id:"206961",title:"Prof.",name:"Junhao",surname:"Xie",slug:"junhao-xie",fullName:"Junhao Xie"},{id:"265429",title:"Dr.",name:"Guowei",surname:"Yao",slug:"guowei-yao",fullName:"Guowei Yao"}],corrections:null},{id:"61717",title:"Use of Historical Google Earth Images to Create Likelihood of Aquatic Plants along Segments of Ohio River",doi:"10.5772/intechopen.77616",slug:"use-of-historical-google-earth-images-to-create-likelihood-of-aquatic-plants-along-segments-of-ohio-",totalDownloads:843,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Aquatic invasive plants are well known for causing severe impacts to local ecosystems, such as degrading water quality, decreasing biodiversity, consuming natural resources, among other impacts. Major water bodies in US had experienced such impacts. To mitigate such impacts, the U.S. Fish and Wildlife Service and the Ohio River Valley Water Sanitation Commission had put significant amounts of effort toward identifying and removing aquatic invasive plants along the Ohio River shorelines. Field work played a significant role in such identification and removal, but at great expense on labor and time. River systems are dynamic, coupled with similarities between spectral reflectance from submerged plants and background water bodies, limited success was reported from literature regarding the use of remote sensing with selected images on detecting aquatic plants. This study utilized Google Earth historical images between 2003 and 2015 along a section of Ohio River known as the Racine Pool and examined and recorded the appearances of aquatic plants. Visible aquatic plants or suspicious submerged objects were digitized and converted to ESRI shapefiles and grids. Spatial analyses and overlays were then performed between grids to derive a map showing frequency of appearance. Such frequency of appearance may serve the purposes of predicting future sighting and/or guides for directing field work in hopes to save labor and time.",signatures:"Yi-Hwa Wu, Ming-Chih Hung, Jeff Thomas and Maureen Gallagher",downloadPdfUrl:"/chapter/pdf-download/61717",previewPdfUrl:"/chapter/pdf-preview/61717",authors:[{id:"184413",title:"Dr.",name:"Ming",surname:"Hung",slug:"ming-hung",fullName:"Ming Hung"},{id:"181853",title:"Dr.",name:"Yi-Hwa",surname:"Wu",slug:"yi-hwa-wu",fullName:"Yi-Hwa Wu"},{id:"250956",title:"Dr.",name:"Jeff",surname:"Thomas",slug:"jeff-thomas",fullName:"Jeff Thomas"},{id:"255978",title:"Mrs.",name:"Maureen",surname:"Gallagher",slug:"maureen-gallagher",fullName:"Maureen Gallagher"}],corrections:null},{id:"58854",title:"Remote Sensing to Detect and Monitor Trees in Various Environments: Case Studies in Chile",doi:"10.5772/intechopen.72903",slug:"remote-sensing-to-detect-and-monitor-trees-in-various-environments-case-studies-in-chile",totalDownloads:1219,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Today, the presence of green areas in cities plays an important role for the well-being of its inhabitants and its sustainable development. Modern cities need green spaces for environmental, psychological, esthetic, economic, and social reasons. In this meaning, Chilean cities, although Chile is still considered by the United Nations1 as a developing economy, are no exception. Given the importance of this common good, it is necessary to optimize its permanent care. An incident factor in the deterioration of vegetation in urban and rural areas is stress, which can be of biotic or abiotic origin. One way to systematize care of trees is by the application of multispectral sensors and modern digital image processing. Once plantations or trees are spectrally characterized, one can proceed to develop plant health mitigation programs. This article shows the potential of remote sensing for tree stress detection, in the central south of Chile. Focus is given on rural areas as forestry for the Chilean economy is of great importance. These approaches can easily be adapted to urban scenarios.",signatures:"Guido Staub",downloadPdfUrl:"/chapter/pdf-download/58854",previewPdfUrl:"/chapter/pdf-preview/58854",authors:[{id:"177001",title:"Associate Prof.",name:"Guido",surname:"Staub",slug:"guido-staub",fullName:"Guido Staub"}],corrections:null},{id:"60691",title:"Monitoring Land Surface Deformation with Satellite ScanSAR Images: Case Studies on Large Earthquakes in China",doi:"10.5772/intechopen.72834",slug:"monitoring-land-surface-deformation-with-satellite-scansar-images-case-studies-on-large-earthquakes-",totalDownloads:999,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter presents a new application of scanning interferometric synthetic aperture radar (ScanSAR) interferometry in monitoring land surface deformation caused by large earthquakes. To make better use of the ScanSAR data and obtain a wider deformation observation, this research studied and analyzed certain key elements of ScanSAR interferometry, including coherence, co-registering, methods of removing orbit errors, correction of atmosphere effects, and geoid undulation. The wide swath mode (WSM) is also known as the ScanSAR mode by which synthetic aperture time is shared by adjacent sub-swaths and azimuth resolution that is traded off for a wider coverage. So, it is possible to monitor a larger area of earthquake deformation. In this study, we obtained ScanSAR and Image Mode (IM) data and analyzed coherence, co-registering, methods of removing orbit errors, correction of atmosphere effects, and geoid undulation to monitor land surface deformation caused by large earthquakes in the 405 × 405 km field of the Wenchuan earthquake and Yutian earthquake, respectively, in China. The results obtained agree well with that of the investigations of the crustal motion in the study areas.",signatures:"Tingchen Jiang, Xiuping Wang, Yuanzhi Zhang and Yu Li",downloadPdfUrl:"/chapter/pdf-download/60691",previewPdfUrl:"/chapter/pdf-preview/60691",authors:[{id:"77597",title:"Prof.",name:"Yuanzhi",surname:"Zhang",slug:"yuanzhi-zhang",fullName:"Yuanzhi Zhang"},{id:"102558",title:"Dr.",name:"Jiang",surname:"Tingchen",slug:"jiang-tingchen",fullName:"Jiang Tingchen"},{id:"212951",title:"Dr.",name:"Yu",surname:"Li",slug:"yu-li",fullName:"Yu Li"}],corrections:null},{id:"60592",title:"Applications of Remote Sensing in Geoscience",doi:"10.5772/intechopen.75995",slug:"applications-of-remote-sensing-in-geoscience",totalDownloads:3857,totalCrossrefCites:3,totalDimensionsCites:6,hasAltmetrics:0,abstract:"Remote sensing is becoming an important and useful tool in mapping large, remote areas and has many applications in geosciences such as geologic and geo-structural mapping, mineral and water exploration, hydrocarbon exploration, natural hazards analysis, and geomorphology. The recent advances in remote-sensing imaging acquisition and availability of images can help geoscientists to explore and prepare maps quickly and evaluate the geo-potential of any specific area on the globe. Advances in remote-sensing data analysis techniques have improved the capacity to map the geological structures and regional characteristics and can serve in mineral exploration in complex and poorly understood regions. In this chapter, geophysical remotely sensed data (airborne geophysics) are integrated with other sources of remotely sensed data to analyze three separate areas, one each for geological structure, lineament presence and orientation, and geothermal potential. Three case studies are discussed in this chapter from three countries—Afghanistan, United Arab Emirates, and Algeria—to show the effectiveness of remote sensing in mapping and detecting geo-structural, geomorphological, and geothermal characteristics of ground surfaces.",signatures:"Hakim Saibi, Mohand Bersi, Mohamed Bodruddoza Mia, Nureddin\nMohamed Saadi, Khalid Mohamed Saleh Al Bloushi and Robert W.\nAvakian",downloadPdfUrl:"/chapter/pdf-download/60592",previewPdfUrl:"/chapter/pdf-preview/60592",authors:[{id:"155975",title:"Dr.",name:"Hakim",surname:"Saibi",slug:"hakim-saibi",fullName:"Hakim Saibi"},{id:"205896",title:"Dr.",name:"Nouredine",surname:"Saadi",slug:"nouredine-saadi",fullName:"Nouredine Saadi"},{id:"205897",title:"Dr.",name:"Mohand",surname:"Bersi",slug:"mohand-bersi",fullName:"Mohand Bersi"},{id:"217413",title:"Dr.",name:"M. Bodruddoza",surname:"Mia",slug:"m.-bodruddoza-mia",fullName:"M. 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Sir Alexander Fleming in his Nobel Prize lecture emphasized the importance of avoiding resistance to antibiotics [1]. Antimicrobial resistance (AMR) is a phenomenon that occurs when infectious microorganisms do not respond to antimicrobial agents, leading to treatment failure, the spread of the infectious disease, and severe illness and death [2]. Among microorganisms, bacteria and fungi are the most encountered pathogens with resistance in clinical settings. Patients infected with resistant bacteria or fungi have worse clinical outcomes compared to patients with infections caused by the same bacteria or fungi without resistance [3]. It is estimated that by the end of year 2050, if unmitigated, AMR will result in 10 million lives lost per year and cumulative cost of 100 trillion USD [4]. The global burden associated with bacterial AMR alone, considering 204 countries and territories, 23 bacterial pathogens, and 88 drug-pathogen combinations, was 4.95 million deaths during the year 2019 [5]. The majority of these patients succumbed to lower respiratory tract and blood stream infections associated with drug-resistant bacteria, with highest mortality rate of 27.3 per 100,000 patients [5]. Among elderly patients in the USA, the treatment of methicillin resistant
The most common bacterial pathogens associated with hospital acquired infections and AMR are the ESKAPE pathogens. ESKAPE is an acronym for
Mechanism of action | Antibacterial class |
---|---|
Inhibitor of cell wall synthesis | β-Lactams, Carbapenems, Cephalosporins, Monobactams, Penicillin, Glycopeptide |
Cell membrane depolarizer | Lipopeptides |
Inhibitor of protein synthesis | Aminoglycosides, Tetracyclines, Chloramphenicol, Lincosamides, Macrolides, Oxazolidinones, Streptogramins |
Inhibitor of nucleic acid synthesis | Quinolones |
Inhibitor of metabolic pathways | Sulfonamides, Trimethoprim |
Inhibitors of ergosterol synthesis | Azoles |
Aqueous pores in cell membrane | Polyenes |
Inhibitor of glucan synthase | Echinocandins |
Inhibitor of squalene epoxidase | Allylamines |
Inhibitor of nucleic acid | 5-Flurocytosine |
Different classes of antibacterial and antifungal drugs and their mechanism of action.
Depicting the difference between intrinsic and acquired resistance. Microorganisms that are intrinsically resistant can propagate from the moment that they are exposed to the antimicrobial agent. Microorganisms can also acquire resistance during exposure to an antimicrobial agent through genetic and nongenetic mechanisms. Adapted from ‘Intrinsic and acquired drug resistance’, by
The emergence of AMR in high-income countries is mainly associated with use, misuse, and overuse of antibiotics in hospitals, agriculture, and communities [29]. Whereas in low- and middle-income countries unhygienic practices, contaminated water supplies, civil conflicts, and an increased number of immunocompromised patients (especially among HIV infections) are the main contributors to AMR [30]. Increased infections, and in turn increased use of antimicrobial agents, has imposed selection pressures that result in the retention of resistant strains. Identifying infectious agents early helps clinicians to promptly choose the appropriate antimicrobial agent to treat the infection based on the intrinsic resistance profiles and local epidemiology data on resistance [31]. Resistance profiling methods, such as culture-based and molecular biology-based methods, currently take up to 72 h from the time of sample collection. During this time, patients often receive broad-spectrum antibiotics, which may lead to acquired resistance (Figure 1). Several novel strategies have been developed for rapid detection of AMR. However, most of these methods are based on molecular biology, immunology, biochemistry, and rapid culture techniques [32]. Importantly, the cost and the expertise involved in establishing and maintaining these techniques and related devices is often too high for many hospitals and institutions, especially those in remote and impoverished communities.
Machine learning (ML) has been around for decades, as optical character recognition gained popularity during 1990s with its application as spam filters. A seminal paper by Geoffery Hinton in 2006 on recognizing handwritten digits using ‘deep learning’ (a ML technique implemented in artificial neural networks) rekindled interest in ML. Recently, during the 14th Critical Assessment of Protein Structure Prediction (CASP14) competition [33], a neural network based model called AlphaFold predicted protein structures with high accuracy (i.e., comparable to the experimental structures), outperforming other protein structural deduction methods [34]. Furthermore, deep learning is increasingly being applied to solve complex multidimensional problems, such as speech recognition [35] and image classification [36].
Machine learning is the application of advanced algorithms that enable a computer to ‘learn’ and generate predictive mathematical models from data. Arthur Samuel in 1959 described ML as ‘the field of study that gives computers the ability to learn without being explicitly programmed’ [37]. Tom Mitchell in 1997 provided a more engineer-oriented definition, when he stated that a ‘computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E’ [38]. Machine learning can be divided into supervised, unsupervised, and reinforcement learning. In supervised learning, the ML model is trained using labeled datasets, with the resulting model being a function that can take new data and predict an output. To determine the reliability of the trained model, a test set of complete input/output data which was not used during training is employed to determine an unbiased estimate of model performance. Whereas, in unsupervised learning, the training data are supplied without labels. Unsupervised learning algorithms find the similarity among data points and cluster them together. Reinforcement learning (RL) uses algorithms that learn from the accumulation of ‘rewards’ that a computational agent receives through interactions with its environment. Reinforcement learning, which is often combined with other ML methods such as deep neural networks, has led to some of the most successful artificial intelligence systems ever developed. These range from systems that beat human professionals in the game of Go [39] to systems that help control nuclear fusion reactions [40].
Recent advances in digitizing medical records and data generated in experiments have paved the way for ML applications in the fields of biology and medicine. Many clinical trials are leveraging ML processes to improve the efficiency and quality of clinical research and pre-clinical drug development [41]. Machine learning is also being applied to assess the risk of developing sepsis based on patients’ clinical records [42]. Machine learning has also found applications at the cellular level. For instance, convolutional neural networks (CNNs) can predict the interactions of transcription factors and histones within chromosome structures, which in turn aids in analyzing genome architecture as well as gene regulation [43]. Other examples include using neural networks to identify the role of non-coding DNA in humans in regulating gene expression [44] and applying recurrent neural networks (RNNs) to characterize chromatin folding in
In this chapter, we first discuss the mechanisms of underlying bacterial and fungal AMR, followed by an overview of ML methods used to detect drug-resistant pathogens. We then highlight the application of ML in the discovery and design of antimicrobial drugs. Finally, we present the challenges and prospects of applying ML to AMR research and drug development.
The major burden of AMR in hospital settings is due to bacteria and fungi. Antimicrobial resistance can be classified into different types, including ‘intrinsic resistance’ and ‘acquired resistance’ (Figure 1) [49]. Intrinsic resistance occurs when bacteria or fungi are naturally resistant to an AMR drug or to a class of AMR drugs [50]. Bacteria and fungi which were previously susceptible to an antimicrobial drug can acquire resistance, for instance, by modifying the target site of the drug or by gaining a resistance mutation (Figure 1). In these scenarios, the microorganism develops resistance post-exposure to the drug. Whereas, if the microorganism does not have a target site for the drug or has a preexisting resistance mutation, then it is classified as intrinsically resistant. Other forms of AMR exist, such as ‘clinical resistance’, whereby a microorganism is susceptible to a drug
Another aspect of AMR is ‘persistence’ and ‘tolerance’, which are phenomena that allow non-growing or slow growing bacterial and yeast pathogens to survive antimicrobial treatment [51, 52]. In the case of genetic resistance to a drug, all the progeny of the resistant microorganism stably inherit resistance to the drug (Figure 1). Whereas persistence occurs when a small fraction of a clonal bacterial population is resistant to an antibiotic, but the persistent cells do not harbor resistance mutations or genes. Rather, these persister cells are in a stationary or dormant phase, which reduces the effectiveness of antibiotics that target growth processes [53, 54, 55]. Antibiotic persistence is a heterogenous response of a bacterial population to an antibiotic and causes a delay in the clearance of the infection [56]. In contrast, tolerant cells require more time to be affected by an antimicrobial drug compared to susceptible cells [56]. Systemic infections due to persistent and tolerant organisms lead to higher mortality rates compared to infections caused by susceptible microorganisms [57]. Nongenetic drug resistance is another form of AMR. Nongenetically drug-resistant phenotypes can be found in clonal cell populations [58] and results from genetically identical cells differentially expressing genes that confer resistance, along with various epigenetic mechanisms [59, 60].
Bacteria and fungi belong to different kingdoms, have differences in cellular components, and antibacterial and antifungal agents target different sites. Despite this, there are similarities between the AMR agents that are used to treat antifungal and antibacterial infections. For instance, cell wall inhibitors of bacteria target peptidoglycan, an important component of the bacterial cell wall, whereas some antifungal agents inhibit ergosterol, an important component of fungal cell membrane. Antibacterial agents have diverse mechanisms of action, including inhibiting cell wall synthesis, depolarizing cell membranes, as well as inhibiting of protein synthesis, nucleic acid synthesis, and metabolic pathways (Table 1) [61]. However, in contrast to many antibacterial agents, antifungal analogues for protein inhibitors, topoisomerase inhibitors, and metabolic pathways inhibitors are not available. Only a limited number of antifungal agents are available that target ergosterol synthesis, cell membrane integrity, glucan synthase, nucleic acid synthesis, and the squalene epoxidase enzyme.
The main mechanisms of antibiotic resistance among bacteria are (i) limiting uptake of a drug; (ii) modifying a drug target; (iii) inactivating a drug; and (iv) active drug efflux (Figure 2a). Limiting uptake due to natural permeability barriers imposed by the cell membrane, drug inactivation by antibiotic inactivating enzymes, and drug efflux resulting non-specific protein efflux pumps are mechanisms of intrinsic resistance. Whereas the transfer of genes between bacteria that encode drug efflux pumps or enzymes that inactivate antibiotics, as well as drug target modifications, are acquired resistance mechanisms. Antibiotic resistance mechanisms differ between gram-negative and gram-positive bacteria due to differences in their cell wall composition. Gram-negative bacteria employ all the drug resistance mechanisms, whereas gram-positive bacteria mainly limit the uptake of a drug [62]. Due to the hydrophobic nature of the cell wall, many of the hydrophilic antibiotic cannot bind to the cell wall and the high lipid content among mycobacteria restricts the entry of hydrophilic antibiotics [63]. However, porin channels found within the cell membrane allow certain hydrophilic antibiotics to enter the cell. Modifications to these porin channels limits drug uptake [64]. Mutations in the gene responsible for porin proteins alter the selectivity of hydrophilic drugs [65]. Drug intake is also restricted by the thickening of cell wall [63]. Another widely observed phenomenon that restricts drug uptake is the formation of bacterial and fungal biofilms. The thick outer layer of a biofilm is composed of extracellular polymeric substances and is impenetrable to many antimicrobial drugs [66].
Mechanisms of action of antimicrobial drugs in bacteria and fungi. (a) Effect of antibacterial drugs on bacterial cellular components and the corresponding resistance mechanism developed by bacteria. Created with
Antibiotics target multiple cellular components and bacteria can modify these targets leading to AMR. One of the major targets is the cell wall, which is commonly targeted by ß-lactam drugs, specifically among gram positive bacteria. Resistance to ß-lactam antibiotics results from modifications in the cell wall structures as well as a number of penicillin-binding-proteins [67]. Bacteria can alter the precursor of the target by mutating the gene responsible for these precursors, eventually leading to an altered target site. This results in the antibiotic failing to bind to the target site [68]. Ribosomes are also commonly targeted by antibiotics to inhibit protein synthesis. Mutations in the ribosomal gene leading to the protection of the ribosomes and methylation of the ribosomal subunits lower the binding affinity of antibiotics, leading to resistance [69]. Similarly, modifications in the DNA gyrase or topoisomerase enzyme, nucleic acid synthesis inhibitors fail to bind to these enzymes [70]. Drugs that inhibit metabolic pathways inhibit important metabolic byproducts that are essential for bacterial survival. These antibiotics competitively bind to the active sites of enzymes responsible for the synthesis essential metabolites. Mutations in the gene responsible for these enzymes restricts antibiotics from binding [71]. Another mechanism of AMR is the inactivation of the drug by the pathogens. Degrading or transferring a chemical group to the antibiotics modifies its structure and affinity towards the target [72]. Efflux pumps remove toxic substances from the bacterial cell; some efflux pumps are constitutively expressed and others are induced or overexpressed in the presence of antibiotics. There are majorly five families of efflux pumps depending on the energy source they utilize and their structure [64]. Namely, the ATP-binding cassette (ABC) family, the multidrug and toxic compound extrusion family, the small multidrug resistance family, the major facilitator superfamily (MFC), and the resistance-nodulation-cell division family. The majority of the bacteria resistant to antibiotics overexpress efflux pumps from one of these families during antibiotics exposure [73].
Antifungal resistance mechanisms are not as extensively studied as antibacterial resistance mechanisms. Several factors including immunosuppressive treatments, indiscriminate use of broad-spectrum antibiotics, and immune suppressive diseases like HIV led to a surge in fungal infections during 1970s and 1980s [74]. Antifungal drugs including imidazoles and azoles were subsequently approved during late 1980s and 1990. Extensive use, misuse, and overuse of these antifungal drugs since then have led to the emergence of AMR in fungal pathogens. Determining if a fungal isolate is resistant is based on the minimum inhibitory concentration (MIC) of the antifungal drug. The MIC of a fungus isolated from a clinical sample informs the decision on the appropriate course of antifungal therapy.
Currently three major classes of anti-fungal drugs used for treating systemic fungal infections. Namely, azoles (itraconazole, voriconazole, posaconazole, and isavuconazole), polyenes (amphotericin B) and echinocandins (caspofungin, micafungin, and anidulafungin) (Table 1). The limited number of classes of antifungal drugs and AMR in fungi restricts treatment options. The emergence of MDR fungal species further hinders treatment options. Azoles target ergosterol biosynthetic pathway, as ergosterol is necessary in the cell membrane to maintain the stability, permeability and the activity of membrane bound enzymes (Figure 2b) [75]. The substitution of an amino acid in the binding site of the enzyme is a common mechanism of azole resistance among
Current methods for detecting AMR among the infecting pathogens take up to 72 h from the time of sample collection. All the isolated bacterial and fungal pathogens must undergo standard antimicrobial susceptibility testing (AST) as recommended by the European Committee on Antimicrobial Susceptibility Testing and the Clinical Laboratory Standards Institute [94, 95]. Early detection of the infecting pathogen along with its drug resistance profile are critical for initiating prompt antimicrobial therapy. However, several challenges are faced during this process, such identifying the pathogen, differentiating between commensal and pathogenic microorganisms in a clinical sample [96]. After successful isolation of the pathogen, a round of subculture must be performed so that contamination can be excluded before commencing AST. Microbroth dilution and disk diffusion AST methods can get delayed due to contamination, leading to delays in initiating the appropriate antimicrobial therapy. Several new technologies and methods are being used for early and rapid detection of AMR. For example, technologies based on nucleic acid amplification, hybridization, microscopy, electrochemical, mass spectroscopy, and nanotechnology [97, 98]. However, these methods require sophisticated instruments, expertise, and expensive consumables restricts their deployment in low-income countries. Point-of-care tests (POCTs) used at patient bedsides are now being used to determine AMR; POCTs can be also used among outpatients. Some types of POCTs like microscopy stations, single molecule biosensors, and microfluidic platforms are being tested [99, 100]. The drawbacks of POCTs, including small sample size, lack of internal standards, and their inability to detect nongenetic forms of AMR resistance still need to be resolved. More advanced methods such as ML approaches to detect AMR could further reduce turn-around times and could be deployed across diagnostic laboratories. Machine learning methods can be also applied to detect certain features that are present in resistant bacteria and fungi, but absent in sensitive isolates, which the human eye or other diagnostic technologies may fail to recognize [101]. For instance, real-time high-throughput screening of modified proteins within the resistant isolates [102] has been less explored and is an ideal application for ML methods. The application of ML methods (Section 3) may lead to a deeper understanding of AMR mechanisms, which in turn could lead to rapidly detecting AMR pathogens in patients (Section 4) and to developing new drugs (Section 5).
Machine learning enables us to investigate and draw conclusions from information contained in data that would otherwise be inaccessible to humans. Problems that benefit from the application of ML are endless, but they have a few defining features [103]. First, the problem may have a known solution, but converting it into a computer program is not feasible or requires extensive resources. For example, humans can easily identify a dog within a group of other four-legged animals but writing a computer program to explicitly describe all possible aspects of a dog and its differences to other similar animals would be error prone and practically infeasible. On the other hand, training a ML algorithm to identify a dog may only take a few lines of code, given modern ML software tools. Second, complex problems where traditional methods have failed to identify a solution may benefit from the use of ML algorithms (Figures 3 and 4), such as the use of deep learning systems to master the game of Go [104] or to make highly accurate predictions of protein structure [34]. Not only does this enable the use of the resulting ML model in practical applications, but it can also guide researchers towards a deeper understanding of the system they are studying. For instance, ML can guide mathematicians by finding patterns and relations between mathematical objects that can lead to the formation of new conjectures and theorems [105].
A selection of common machine learning methods. (A) Linear regression model using a prediction line to distinguish the test dataset. (B) Logistic regression model using a threshold to distinguish the test dataset into two groups. (C) Random forest model using a visually generated decision tree for datapoints to estimate each samples outcome by voting. (D) Multilayer perceptron architecture consisting of an input layer, multiple hidden layers, and an output layer.
The machine learning pipeline. This pipeline consists of data originating from different biological experiments, preprocessing steps for cleaning the data, along with the feature extraction process. Machine learning methods are then applied to the clean data by dividing this data into training, testing, and validation sets. ‘MALDI TOF’ stands for ‘matrix assisted laser desorption ionization time of flight’, ‘LR’ for ‘logistic regression, ‘CNN’ for ‘convoluted neural network, ‘SVM’ for ‘support vector machine’, and ‘RF’ for ‘random forest’.
Although the defining feature of all ML approaches is to learn from a given dataset, ML techniques can be separated into three broad categories based on the amount of human input: Supervised learning, unsupervised learning, and reinforcement learning [103, 106, 107, 108]. Each of these approaches have their own concepts, techniques, and areas of applicability, with the differences between them not always clear. Nonetheless, these categories are useful to provide a means to determine the best approach for a particular problem at hand. Understanding the available tools is crucial for choosing the best ML technique to solve a particular problem. Although an extensive overview of each ML category is outside the scope of this chapter, we provide an overview of some of the common ML methods below.
Supervised learning consists of algorithms that learn using a training set consisting of labeled data [106]. The goal of supervised learning is to find a model for the relationship between the inputs (called ‘features’) and known outputs, which can then be used to predict outputs for future inputs, where the actual outputs are unknown. Supervised learning techniques can be separated into two categories, ‘classification’ and ‘regression’ [109, 110].
Classification problems generally aim to classify future inputs into predefined categories through training on examples, where the inputs are labeled with their corresponding category [107]. Given enough quality training data, models created with classification techniques can provide accurate classification of future data, without requiring the details of the input data to be explicitly programed [103, 106, 107, 108]. For instance, a researcher may desire to have a computer take a microscopy image of a cell and return the name of the species, without requiring a human to identify the species. Using a training set of microscopy images for a variety of different species labeled with the name of the species, a classification model can be trained to learn the relationships between the visual aspects of the species and their labels. The model produced can then be used on unlabeled microscopy images to determine the species, saving researchers time and effort, along with producing a model that can be shared in the scientific community. Classification learning algorithms are not restricted to images; any form of data that can be separated into predefined categories can be fed into a classification learning algorithm for training to produce a classifier model [107, 108].
While classification methods aim to predict discrete class labels for inputs, regression methods aim to predict continuous numerical values for given numerical inputs [107, 108]. Regression techniques also learn from training data containing inputs and outputs, but in this case the data consists of numerical inputs and their corresponding numerical outputs, with the resulting model being a continuous mathematical relationship between inputs (independent variables) and outputs (dependent variables) [107]. The resulting model can then be provided with future inputs to make numerical predictions. For example, a researcher may be interested in finding a mathematical relationship between the inputs of an experiment (e.g., preset voltages) and the corresponding outputs they detect (e.g., electrical currents), for systems where theory is unable to make accurate predictions. By training a regression model on a large amount of set inputs and detected outputs, the researcher may be able to find a model that accurately predicts numerical outputs when given future inputs. Not only is this useful in a practical sense, but the resulting model can also be used to guide fundamental research by providing an accurate mathematical and physical relationships that can be further analyzed and understood in terms of theoretical ideas [105, 111].
Through extensive research on supervised learning, many different learning algorithms for classification and regression have been developed and programmed into readily available software packages. Linear regression, logistic regression [107, 108], support vector machines (SVMs) [112], decision trees and random forests [113] and most artificial neural networks [114] are some examples of supervised learning systems, each having their own advantages and disadvantages.
Unsupervised learning methods, unlike supervised learning, attempt to learn from unlabeled data [115]. This often takes the form of data clustering, but other methods such as anomaly detection and dimensionality reduction also fall under this category [107, 108]. Clustering algorithms attempt to separate unlabeled data into groups with similar components, which can be useful for extracting information from high-dimensional data, which is often infeasible for a human to do. Anomaly detection involves finding anomalous outliers in large datasets by comparing data points to learned patterns, which can be helpful when working with noisy experimental data [116, 117]. Dimensionality reduction methods attempt to simplify high-dimensional data without losing important information, making the analysis and use of such data easier [118, 119]. Unsupervised learning methods can also be combined with supervised learning, referred to as ‘semi-supervised’ learning, to learn from data that is partially labeled [120, 121]. This is useful when working with large amounts of data, where labeling every data point is infeasible. Some examples of unsupervised learning methods include k-means clustering [122, 123], hierarchical clustering [124, 125], DBSCAN [126], isolation forests [127], principal component analysis [128], autoencoders [107, 108], locally linear embedding [129], and expectation-maximization algorithms [130].
Reinforcement learning approaches rely on the idea of learning from ‘rewards’ obtained through interactions with an environment [131]. Reinforcement learning problems are formulated as a discrete-time stochastic control processes known as ‘Markov decision processes’, with the goal of training a computational system (or ‘agent’) to determine the best strategy (or ‘policy’) for reaching a defined goal [132]. The environment is defined by ‘states’ that the agent can be in, while the agent is able to perform certain ‘actions’ to interact with the environment. As the agent interacts with its environment, numerical values called rewards that model performance are collected for performing certain actions [132]. The goal of the agent is then to maximize these rewards (using sophisticated statistical methods) by learning the best policy for making decisions in particular situations through repeated interactions with its environment [132]. For example, a reinforcement learning system may be programmed into a cleaning robot to maximize the amount of cleaning it can do while still being able to return to its charging station. In this case, a positive reward would be given for picking up trash, while a negative reward would be given for letting its battery die without reaching the charging station. Using reinforcement learning methods, the robot can learn to optimize its own behavior through repeated experience with its environment.
To ensure the model created using ML is accurate it must be validated on data independent of the training set [103, 106, 107, 108, 133]. Applying the trained model directly to a certain problem is one method of testing, but this is often impractical for real-world applications where model performance matters. The usual method of validation is to split the initial dataset into training and testing sets, where the model is trained on the training set and its accuracy is determined by comparing its predictions using the testing set inputs to the true outputs from the test set [107, 108]. This analysis provides the ‘generalization error’ estimate of the model, which is used to determine whether the model is accurate, and the errors associated with using the model on new data [107]. Many different metrics are used to determine the generalization error, such as the root mean square error or false-positive/false-negative rates [103, 107, 108], and the choice of method depends on the problem and the learning algorithm. Through iterative training and testing cycles, model performance is improved until a satisfactory accuracy is achieved.
A major issue when using ML is overfitting the model to the training set [103, 106, 107, 108, 133]. This corresponds to the case where the ‘training error’ (i.e., how well the model matches the training data) is low, but the generalization error (i.e., how well the model can predict outcome values for previously unseen data) is high [107, 108]. This is a common occurrence, especially when using models that are more complex than the actual relationships contained in the data. For example, if the actual relationship between inputs and outputs is linear but we attempt to fit a third-degree polynomial to the data, we may produce a model that passes through each of the training set data points exactly (low training error) but cannot generalize to data outside of the training set (high generalization error). Avoiding overfitting (as well as underfitting) requires the use of appropriate training and validation methods to determine model performance before deploying a trained ML model. The quantity of training data is also important. A lack of training data can lead to inaccurate or biased predictions. The amount of data required to create accurate models ultimately depends on the problem and ML method being used [103, 106, 107, 108, 133].
During the testing stage, it is important to tune the ‘hyperparameters’ of the model to improve training accuracy [103, 106, 107, 108, 133, 134, 135]. Hyperparameters refer to the parameters that are not being learned, such as gradient time steps or data batch size. Many cross-validation techniques for hyperparameter tuning are available, such as k-fold cross validation [135], and can be implemented directly in ML software packages. It is also often necessary for datasets to be pre-processed before applying ML techniques [136]. Pre-processing is application/software dependent and involves converting the collected data into data structures that can be read by the ML algorithm/software package being used.
The extensive and increasing use of ML in industry and scientific research has led to the development of many tools for applying ML techniques quickly and accurately. With almost every well-established ML algorithm being implemented in free dedicated software packages, deploying a ML solution has in some cases become as simple as writing a few lines of code. Although the researcher must determine whether their problem may benefit from the application of ML, the availability of extensively tested and optimized tools to apply ML has made doing so much easier once the relevant data has been collected and organized.
Python is currently the most used programming language for ML, as it contains well-developed and optimized ML libraries. However, other languages such as Julia are also becoming popular with ML researchers. Below is a list of some of the free software packages used for ML applications, along with the programming languages they can be used with.
TensorFlow (https://www.tensorflow.org/) [137]. Developed by Google, TensorFlow can be used with a variety of programming languages, including Python, C++, Julia, and Java.
Keras (https://keras.io/) [138]. Keras is a widely used, user-friendly Python interface for the TensorFlow library.
Scikit-learn (https://scikit-learn.org/) [139]. Scikit-learn is a Python library that contains many ML algorithms, optimized for Python data structures. Wrappers to use Scikit-learn with other programming languages, such as Julia, are also available.
PyTorch (https://pytorch.org/) [140]. Developed by Facebook, PyTorch is a ML framework primarily for Python, but it also has a C++ version.
Over the last decade, an increase in AMR has occurred across the world. At the same time, ML methods have been successfully applied in numerous scientific fields. The availability of large datasets from whole genome sequencing (WGS), matrix assisted laser desorption ionization time of flight mass spectroscopy (MALDI TOF MS), transcriptional response to antibiotics and proteome profiles have facilitated the application of ML algorithms to detect AMR. Specifically, ML methods have been used to detect AMR in bacterial and fungal pathogens based on the data obtained from WGS and MALDI TOF MS (Figure 4) [102, 141, 142, 143]. Reduced genomic sequencing cost and high-throughput data from WGS has enabled application of ML methods to sequence data. A few studies have utilized genome sequencing data to predict resistance phenotypes among bacterial pathogens using ML methods [144, 145, 146, 147, 148, 149]. A ML method called ‘adaptive boosting’ was employed to detect carbapenem resistance in
The success rate of a potential therapeutic drug is extremely very low. Between 2000 and 2015, the success rate of drug development in oncology alone was as low as 3.4% [154]. Drug discovery involves various steps from target identification, optimization, validation, and hit discovery [155]. Machine learning is being implemented in the drug discovery process, from identifying the potential molecules or compounds against a particular disease to clinical trials [156]. A new drug, from its discovery through to clinical trials, involves huge cost (approximately 2.5 billion USD) and may take up to 10–15 years to come to market [157, 158]. The advent of high-throughput screening methods and the associated ‘omics’ data, along with the computer-assisted drug design (CADD) technologies, encouraged pharmaceutical companies to focus on leveraging ML methods to identify potential drug targets as well as new drugs. These
The first step in the drug discovery is to associate the target with the disease of interest. Here, it is hypothesized that inhibiting or modifying the target results in the alleviation of the disease. Machine learning has been applied to find the target using protein-protein, transcriptional, and metabolic interactions within cells and tissues. In this regard, semi-supervised learning models based on drug-protein interaction network information, chemical structures and genomic sequence data were able to predicted drug-protein interactions on enzyme, ion channel, GPCR (G protein coupled receptor), and nuclear receptor datasets [159]. A decision tree-based meta-classifier was employed to predict genes based on the aforementioned interactions that are associated with morbidity and that can be used as targets [160]. Similarly, a SVM model was able to classify proteins as drug targets and non-drug targets, for breast, pancreatic, and ovarian cancers [156]. In this study, after predicting multiple targets, two of the predicted targets were validated using peptide inhibitors, which had antiproliferative activity on cell culture models. Other studies have utilized ML methods for identifying drug targets, including for Huntington’s disease [161]. The drug-protein interaction (DPI) databases consist of drugs that interact with therapeutic protein targets. However, these drugs might interact with the non-target proteins
Support vector machines have been extensively used in drug development. The SVM method has been applied to raw data to predict the radiation protection function and toxicity for radioprotectors targeting p53 [163]. A regression-SVM model was used to assess target-ligand interactions [164]. Support vector machines were also able to predict the ‘druggability’ based on the structure of target [165] and have been used for other applications such as identifying drug-target interaction [109], cancer cell properties, drug resistance [110], selection of therapeutic compounds from public database [166], predicting properties of organic compound [167], designing new ligands [168], and virtual screening [169]. Random forest algorithms have been used to improve scoring function performance in ligand-protein binding affinity [169]. Random forest approaches have also been used to select molecular descriptors to achieve better accuracy for the compounds designed for drugs used in immune network technology [170]. Multilayer perceptron (MLP) algorithm is another ML approach that has been mainly used to generate compounds automatically for
Machine learning approaches have been used to discover antibiotics. Stokes et al. discovered an antibiotic from the ‘Drug Repurposing Hub’ called halicin. This drug is effective against
Antimicrobial resistance is an emerging global health crisis. As infectious microorganisms are evolving resistance through genetic and nongenetic mechanisms, new methods are required to rapidly diagnose and treat drug-resistant infections. The recent discovery of novel forms of AMR, including tolerance, persistence, and nongenetic resistance highlights the ingenuity of pathogenic microorganisms as well as the multifaceted nature of this problem. Digitization of clinical records presents opportunities for leveraging ML methods for fast and accurate identification of resistant microorganisms. However, applying ML methods to detect AMR is still in the nascent stage. Importantly, the quantity and quality of the data required to detect resistance among bacteria and fungi are still limited. Furthermore, ML models currently used elsewhere require optimization to successfully detect AMR. Advancement in the areas of laboratory diagnosis of infectious agents and sharing of data across different centers could pave the way forward for using ML methods identify and detecting drug-resistant microorganisms.
Machine learning has played an important role in the discovery of drugs by identifying novel drug targets and drug molecules. Several new drugs discovered using ML methods have been successful in clinical trials after spending comparatively less time in the drug discovery pipeline. Though ML methods are proving to useful in drug design and drug discovery, several challenges still exist. For instance, the absence of sufficient training data as well as biased, faulty, or noisy training data results in poor ML model predictions. To address this, methods to remove outliers, and filter out unwanted features are being developed to increase the predictive power of ML models.
Another issue is that ML algorithms employ a ‘black box’ approach to train ML models. Specifically, how the features are being interpreted during each stage of the training to come to an accurate prediction is largely still not understood. An area of research called explainable artificial intelligence (XAI) has emerged to address this issue. XAI consists of processes and methods that help the human users to comprehend the results generated by ML algorithms. Also, XAI helps to characterize the model accuracy, transparency, and outcomes [190]. Applying XAI in the field of AMR research may lead to the discovery of novel resistance mechanisms. Finally, the heterogeneity of many databases restricts the incorporation of ML algorithms to these databases. However, the data on disease, drug compounds, and AMR mechanisms are growing day-by-day, leading to the continuous curation of ML models. Other challenges for deploying ML algorithms include cross-platform normalization, statistical issues, and the division of testing datasets. Many of these issues may be resolved through sophisticated data preprocessing methods. Importantly, these data and interpretability issues will need to be resolved before ML methods are more widely adopted in scientific research and trusted in clinical settings.
DC was supported by a seed grant from AI4Society and funding from University of Alberta.
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These reactions occur through a regular radical chain causing growth of polymer by three steps, namely, initiation, propagation, and termination. To understand ionizing radiation-induced polymerization, the water radiolysis must be taken into consideration. This chapter explores the mechanism of water molecules radiolysis paying especial attention to the basic regularities of solvent radicals’ interaction with the polymer molecules for forming the crosslinked polymer. Water radiolysis is the main engine of the polymerization processes, especially the “free-radical polymerization.” The mechanisms of the free-radical polymerization and crosslinking will be discussed in detail later. Since different polymers respond differently to radiation, it is useful to quantify the response, namely in terms of crosslinking and chain scission. A parameter called the G-value is frequently used for this purpose. It represents the chemical yield of crosslinks, scissions and double bonds, etc. For the crosslinked polymer, the crosslinking density increases with increasing the radiation dose, this is reflected by the swelling degree of the polymer while being immersed in a compatible solvent. If crosslinking predominates, the crosslinking density increases and the extent of swelling decreases. If chain scission predominates, the opposite occurs. 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The natural resources are limited, and due to the natural disasters like sudden and severe abiotic stress factors, excessive floods, etc., the production capacities are changed per year. In contrast, the yield potential should be significantly increased to cope with this problem. Despite rich genetic diversity, manipulation of the cultivars through alternative techniques such as mutation breeding becomes important. Radiation is proven as an effective method as a unique method to increase the genetic variability of the species. Gamma radiation is the most preferred physical mutagen by plant breeders. Several mutant varieties have been successfully introduced into commercial production by this method. Combinational use of in vitro tissue culture and mutation breeding methods makes a significant contribution to improve new crops. Large populations and the target mutations can be easily screened and identified by new methods. Marker assisted selection and advanced techniques such as microarray, next generation sequencing methods to detect a specific mutant in a large population will help to the plant breeders to use ionizing radiation efficiently in breeding programs.",book:{id:"5451",slug:"new-insights-on-gamma-rays",title:"New Insights on Gamma Rays",fullTitle:"New Insights on Gamma Rays"},signatures:"Özge Çelik and Çimen Atak",authors:[{id:"147362",title:"Dr.",name:"Özge",middleName:null,surname:"Çelik",slug:"ozge-celik",fullName:"Özge Çelik"},{id:"147364",title:"Prof.",name:"Çimen",middleName:null,surname:"Atak",slug:"cimen-atak",fullName:"Çimen Atak"}]},{id:"32846",doi:"10.5772/36950",title:"Current Importance and Potential Use of Low Doses of Gamma Radiation in Forest Species",slug:"current-importance-and-potential-use-of-low-doses-of-gamma-radiation-in-forest-species",totalDownloads:5281,totalCrossrefCites:2,totalDimensionsCites:13,abstract:null,book:{id:"1590",slug:"gamma-radiation",title:"Gamma Radiation",fullTitle:"Gamma Radiation"},signatures:"L. 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In this chapter, the following aspects of radiation treatment process are considered: sources of contamination and major contaminants of water and wastewater; primary processes in aqueous systems initiated by ionizing radiation; principal ways of contaminant conversion as consequences of primary processes (complete mineralization of organic compounds, partial decomposition of organic molecules resulted in detoxification, decolorization, disinfection of polluted water, and improvement in biological degradation of contaminant, polymerization of monomers’ contaminants, oxidation-reduction processes, and coagulation of colloids); sources of ionizing radiation; and main equipment applied in radiation technologies of aqueous system purification.",book:{id:"6149",slug:"ionizing-radiation-effects-and-applications",title:"Ionizing Radiation Effects and Applications",fullTitle:"Ionizing Radiation Effects and Applications"},signatures:"Igor E. Makarov and Alexander V. Ponomarev",authors:[{id:"213652",title:"Dr.",name:"Igor",middleName:null,surname:"Makarov",slug:"igor-makarov",fullName:"Igor Makarov"},{id:"213657",title:"Dr.",name:"Alexander",middleName:null,surname:"Ponomarev",slug:"alexander-ponomarev",fullName:"Alexander Ponomarev"}]}],mostDownloadedChaptersLast30Days:[{id:"32842",title:"Sterilization by Gamma Irradiation",slug:"sterilization-by-gamma-irradiation",totalDownloads:74766,totalCrossrefCites:37,totalDimensionsCites:85,abstract:null,book:{id:"1590",slug:"gamma-radiation",title:"Gamma Radiation",fullTitle:"Gamma Radiation"},signatures:"Kátia Aparecida da Silva Aquino",authors:[{id:"102109",title:"Dr.",name:"Katia",middleName:"Aparecida Da S.",surname:"Aquino",slug:"katia-aquino",fullName:"Katia Aquino"}]},{id:"32837",title:"Environmental Gamma-Ray Observation in Deep Sea",slug:"environmental-gamma-ray-observation-in-deep-sea-",totalDownloads:2917,totalCrossrefCites:4,totalDimensionsCites:6,abstract:null,book:{id:"1590",slug:"gamma-radiation",title:"Gamma Radiation",fullTitle:"Gamma Radiation"},signatures:"Hidenori Kumagai, Ryoichi Iwase, Masataka Kinoshita, Hideaki Machiyama, Mutsuo Hattori and Masaharu Okano",authors:[{id:"108174",title:"Dr.",name:"Hidenori",middleName:null,surname:"Kumagai",slug:"hidenori-kumagai",fullName:"Hidenori Kumagai"},{id:"108237",title:"Dr.",name:"Masa",middleName:null,surname:"Kinoshita",slug:"masa-kinoshita",fullName:"Masa Kinoshita"},{id:"137650",title:"Dr.",name:"Ryoichi",middleName:null,surname:"Iwase",slug:"ryoichi-iwase",fullName:"Ryoichi Iwase"},{id:"137656",title:"Dr.",name:"Hideaki",middleName:null,surname:"Machiyama",slug:"hideaki-machiyama",fullName:"Hideaki Machiyama"},{id:"146918",title:"Dr.",name:"Mutsuo",middleName:null,surname:"Hattori",slug:"mutsuo-hattori",fullName:"Mutsuo Hattori"},{id:"146919",title:"Dr.",name:"Masaharu",middleName:null,surname:"Okano",slug:"masaharu-okano",fullName:"Masaharu Okano"}]},{id:"58998",title:"Ionizing Radiation-Induced Polymerization",slug:"ionizing-radiation-induced-polymerization",totalDownloads:1784,totalCrossrefCites:8,totalDimensionsCites:17,abstract:"Ionizing radiation can induce some kinds of reactions, other than polymerization, such as dimerization, oligomerization, curing, and grafting. These reactions occur through a regular radical chain causing growth of polymer by three steps, namely, initiation, propagation, and termination. To understand ionizing radiation-induced polymerization, the water radiolysis must be taken into consideration. This chapter explores the mechanism of water molecules radiolysis paying especial attention to the basic regularities of solvent radicals’ interaction with the polymer molecules for forming the crosslinked polymer. Water radiolysis is the main engine of the polymerization processes, especially the “free-radical polymerization.” The mechanisms of the free-radical polymerization and crosslinking will be discussed in detail later. Since different polymers respond differently to radiation, it is useful to quantify the response, namely in terms of crosslinking and chain scission. A parameter called the G-value is frequently used for this purpose. It represents the chemical yield of crosslinks, scissions and double bonds, etc. For the crosslinked polymer, the crosslinking density increases with increasing the radiation dose, this is reflected by the swelling degree of the polymer while being immersed in a compatible solvent. If crosslinking predominates, the crosslinking density increases and the extent of swelling decreases. If chain scission predominates, the opposite occurs. A further detailed discussion of these aspects is presented throughout this chapter.",book:{id:"6149",slug:"ionizing-radiation-effects-and-applications",title:"Ionizing Radiation Effects and Applications",fullTitle:"Ionizing Radiation Effects and Applications"},signatures:"Mohamed Mohamady Ghobashy",authors:[{id:"212371",title:"Dr.",name:"Mohamed",middleName:null,surname:"Mohamady Ghobashy",slug:"mohamed-mohamady-ghobashy",fullName:"Mohamed Mohamady Ghobashy"}]},{id:"53780",title:"Gamma-Ray Spectrometry and the Investigation of Environmental and Food Samples",slug:"gamma-ray-spectrometry-and-the-investigation-of-environmental-and-food-samples",totalDownloads:2501,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Gamma radiation consists of high‐energy photons and penetrates matter. This is an advantage for the detection of gamma rays, as gamma spectrometry does not need the elimination of the matrix. The disadvantage is the need of shielding to protect against this radiation. Gamma rays are everywhere: in the atmosphere; gamma nuclides are produced by radiation of the sun; in the Earth, the primordial radioactive nuclides thorium and uranium are sources for gamma and other radiation. The technical enrichment and use of radioisotopes led to the unscrupulously use of radioactive material and to the Cold War, with over 900 bomb tests from 1945 to 1990, combined with global fallout over the northern hemisphere. The friendly use of radiation in medicine and for the production of energy at nuclear power plants (NPPs) has caused further expositions with ionising radiation. This chapter describes in a practical manner the instrumentation for the detection of gamma radiation and some results of the use of these techniques in environmental and food investigations.",book:{id:"5451",slug:"new-insights-on-gamma-rays",title:"New Insights on Gamma Rays",fullTitle:"New Insights on Gamma Rays"},signatures:"Markus R. Zehringer",authors:[{id:"311750",title:"Dr.",name:"Markus R.",middleName:null,surname:"Zehringer",slug:"markus-r.-zehringer",fullName:"Markus R. Zehringer"}]},{id:"54118",title:"Gamma Rays from Space",slug:"gamma-rays-from-space",totalDownloads:2052,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"An overview of gamma rays from space is presented. We highlight the most powerful astrophysical explosions, known as gamma-ray bursts. The main features observed in detectors onboard satellites are indicated. In addition, we also highlight a chronological description of the efforts made to observe their high energy counterpart at ground level. Some candidates of the GeV counterpart of gamma-ray bursts, observed by Tupi telescopes, are also presented.",book:{id:"5451",slug:"new-insights-on-gamma-rays",title:"New Insights on Gamma Rays",fullTitle:"New Insights on Gamma Rays"},signatures:"Carlos Navia and Marcel Nogueira de Oliveira",authors:[{id:"189908",title:"Dr.",name:"Carlos",middleName:null,surname:"Navia",slug:"carlos-navia",fullName:"Carlos Navia"},{id:"243084",title:"MSc.",name:"Marcel",middleName:null,surname:"De Oliveira",slug:"marcel-de-oliveira",fullName:"Marcel De Oliveira"}]}],onlineFirstChaptersFilter:{topicId:"227",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"25",title:"Environmental Sciences",doi:"10.5772/intechopen.100362",issn:"2754-6713",scope:"
\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems.
\r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.