Sample size calculation techniques are provided.
\r\n\tThis book will aim at the biodegradation of natural and artificial waste streams to value-added products using various biotechnological approaches of bioremediation, biocatalysis, bioengineering, bioprocessing, and nanotechnology for human benefits, including food, fuel, agriculture, environment, and healthcare sectors.
",isbn:"978-1-83768-078-8",printIsbn:"978-1-83768-077-1",pdfIsbn:"978-1-83768-079-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"8241477a4a935c8c292902d2768f4581",bookSignature:"Dr. Vasudeo Zambare and Dr. Mohd Fadhil Md Din",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12043.jpg",keywords:"Microbial Biodegradation, Dye Degradation, Biocatalyst and Biocatalysis, Lignocellulosic Biomasses, Biofuels and Biochemicals, Metabolic Engineering, Genetic Engineering, Food Waste, Agro Waste, Waste Recycling, Industrial Waste Bioconversion, Nanoparticles in biodegradation",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 17th 2022",dateEndSecondStepPublish:"June 14th 2022",dateEndThirdStepPublish:"August 13th 2022",dateEndFourthStepPublish:"November 1st 2022",dateEndFifthStepPublish:"December 31st 2022",remainingDaysToSecondStep:"21 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Vasudeo Zambare is a Senior Scientist at Om Biotechnologies, INDIA. Dr. Zambare completed his Ph.D. degree in Biochemistry from Agharkar Research Institute of Pune University, Pune, Maharashtra (India), in 2007. He has made 3 patents and over 200 technical and scientific contributions (book chapters, books, patents, proceedings, popular articles, nucleotide sequences, conferences, and workshops).",coeditorOneBiosketch:"Dr. Mohd Fadhil Md Din is a Director of Campus Sustainability at Universiti Teknologi Malaysia, Malaysia. He has made over 278 technical and scientific contributions (book chapters, books, proceedings, popular articles, conferences, and workshops). He is honored by several funding grants of Million 4.35 MYR and 18 national-international prestigious awards.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"174690",title:"Dr.",name:"Vasudeo",middleName:null,surname:"Zambare",slug:"vasudeo-zambare",fullName:"Vasudeo Zambare",profilePictureURL:"https://mts.intechopen.com/storage/users/174690/images/system/174690.jpg",biography:"Dr. Vasudeo Zambare is a Senior Scientist in the Departments of R & D, Om Biotechnologies, Nashik, Maharashtra, INDIA. Dr. Zambare completed his Ph.D. degree in Biochemistry from Agharkar Research Institute of Pune University, Pune, Maharashtra (India) in 2007. Dr. Vasudeo Zambare is a Senior Scientist at Om Biotechnologies, INDIA. He has made 3 patents and over 200 technical and scientific contributions (book chapters, books, patents, proceedings, popular articles, nucleotide sequences, conferences, workshops) and over 70 peer-reviewed journal articles. Dr. Zambare is a multi-skilled researcher with biorefinery-industry experience in the US, Canada, EU, and India. He has developed bioprocesses for the leather, textile, paper and pulp, and biofuel industries. More than 15 years of achievements advancing knowledge by devising fermentation process development, assays and analytical methods to solve complex research problems with potential commercial applications (biofuel, food and pharmaceuticals). 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Precisely in 1858, Hoppe published a report on the discovery of arsenic in the hair of a human corpse exhumed after 11 years [1, 2]. Almost a decade later, amphetamine was discovered in the fur of a guinea pig by Goldblum et al., in 1954 [3]. In the late 1970s, Baumgartner et al., developed what is now known as a radioimmunoassay (RIA) kit for the detection of opiates in hair [4]. Thus, laying the groundwork for the first contemporary use of hair in drug testing. This method which was first introduced to Germany by Arnold in 1980 generated a lot of controversies [5]. However, subsequent work by Klug [6] that same year provided a basis for the use of hair in forensic toxicology by confirming the RIA method with thin-layer chromatography and fluorescence detection. Also, the inception of gas chromatography with mass spectroscopy (i.e. GC/MS) 6 years later, led to improved detection, sensitivity and specificity at low cost. Therefore, increasing the number of newly discovered compounds [1]. Over the next three decades, advances in chromatographic and spectrometric techniques coupled with new methods of sample preparation and wash procedures have enhanced the detection limits from ng/mg range to pg/mg [7]. Microscopic hair analysis, as another tool for hair testing, only began to gain a rapid recognition in the early twentieth century. For instance, the books published by Glaister in 1931 on the study of hairs and wools on mammals [8], and that of Hick on the microscopy of hairs in 1971 [9] both became a widely used resource for the forensic scientist in the use of hair as evidence in a crime scene. Preliminary findings from hair testing led to the American scandal of false imprisonment from evidence based on hair microscopy [10], which lead to severe scepticism on the validity of hair testing for analytical or forensic purposes.
\nNonetheless, the scientific use of hair analyses and testing as a tool for forensic toxicology and medicolegal purposes, in the era of precision medicine, has proved valuable. More so, the recent need for a non-invasive method/testing substrates for early diagnosis and profiling of diseases based on their biology and molecular phenotypes [11], has made hair testing an attractive option. Also, application of non-invasive strategies improves patient compliance. Considering that the human hair is mostly composed of proteins; it is an attractive proteomic substrate for diagnostic and forensic pathology. Not least, the presence of other biomolecules (such as lipids) in hair indicates that an integrated multi-omics approach would significantly improve the field of investigative medicine [12].
\nIt has been hypothesized that irrespective of ethnicity or hair form, there is biochemical and growth rate similarities in all hair types [13]. However, emerging evidence seems to suggest that differences exist in hair structure and growth rates depending on ethnic profile or grooming [14]. Human hair distribution and growth is androgen-dependent resulting in differences between males and females as well as regional variation within the same individual [15].
\nThe human hair follicle contains stem cells in their bulge region and in the dermal papilla [16]. These stem cells are responsible for the significant self-renewal capabilities of the human hair follicle and the hair cycle. There are remarkable disparities in molecular structure and biology of human hair based on individual differences, gender, age, various hair grooming habits; chemotherapy/radiation exposure; cosmetics as well as diseases [17].
\nOmics-based techniques employ a holistic interrogation of the full complement of a biomolecule in a systems biology oriented manner. Although, many omics-based methods are yet to be employed routinely in hair testing and biomarker identification; there is reasonable hope that they will provide much needed insight to the biology and pathology of several medical conditions. This review intends to evaluate the current hair testing applications and potential benefits of hair omics-based approaches in diagnostic medicine.
\nHuman hair is a long, thin cylinder of keratinized cells that grows from large cavities or sacs called follicles. Hair is usually composed of three distinct regions: an inner cortex, an external cuticle and occasionally a central inconsistent medulla or core running along a central axis of thicker terminal hairs [18]. The cortex is primarily responsible for the mechano-physical properties of the hair fiber and is composed of long filaments called microfibrils which contain organized α-helical rods of keratin, embedded in an amorphous matrix [19] (Figure 1). Human hair is composed of 65–95% proteins, 15–35% water and 1–9% lipids, 0.1–5% pigments (melanin), small amounts of trace elements, and polysaccharides [18]. Older studies suggest there is no significant difference in the amino acid composition of hairs from different racial group [20]; however, there is evidence that there are significant differences in the lipid composition of hair [21]. The hair fiber is rich in lipids either derived from sebum or secreted from the apocrine gland which consists of free fatty acids, mono-di-and triglycerides, wax esters, hydrocarbons, and alcohols [22].
\nComplex structure of the human hair fiber (picture adapted with kind permission [
On average, human scalp hair is reported to grow approximate 1 cm/month. However, recent data reports lower growth rates for hair originating from people of African ancestry. The human hair growth cycle consists of four phases called anagen (growth), catagen (transition), telogen (rest) and exogen (shedding phase) [23, 24]. During the anagen phase, the capillary blood supply around the follicle provides nutrients and delivers any extraneous materials that may be present in the blood stream such as drugs or toxins [10]. Although the mechanism of the incorporation of drugs or metabolites into hair is not clear, three pathways have been proposed and are generally accepted by scientists including: active or passive diffusion of drugs into the bloodstream feeding the dermal papilla, diffusion from sweat or other excretions the growing or mature fiber is exposed to and, external diffusion from vapors or powders into the mature hair fiber [25]. However, the relative importance of each pathway has not been elucidated and it is probable that the incorporation of drugs into hair may involve a complex series of events which may conceivably vary between different individuals.
\nIn comparison with blood sample collection, hair collection is easy and non-invasive. It does not require any specialized equipment or storage facilities. It permits long-term storage at room temperature without any risk of contaminating the environment. There is reasonable evidence that using hair as a testing substrate in medicine is of tremendous benefit and that there are many unexplored possibilities to the use of hair testing in the evidence-based era of precision medicine. However, it is important to comment about the reliability of hair testing as this has far reaching ramifications in various scenarios. In a study that assessed the reliability of inter-laboratory and intra-laboratory variations in hair minerals using three different laboratories, a consistent numerical result was generated from all three laboratories [26]. However, the results were interpreted differently because each laboratory used different normal reference ranges. This indicates that variation in reported concentrations of specific analytes in hair would result in different interpretation of the patient’s health status in each instance. Therefore, effective ancillary use of hair in diagnostic medicine requires standardization of normal reference ranges. Over- or under-reporting may be another factor that might affect the reliability of the use of hair as an alternative biological testing matrix. For instance, Vignali et al., found discrepancies between self-reported use and biological testing of illicit drugs use among inmates [27]. The authors explained that theses discrepancies might be due to false declaration of drug use in hope of qualifying for entry into rehabilitation programs. Also, illicit drug users with moderate risk may use levels below the limit of detection because there is inadequate empirical evidence of standardized minimum detectable levels of specific substances of abuse; hence, a negative test does not connote “no exposure” [28]. On the other hand, hair testing has become so reliable that it was considered better than urine, blood or breath testing, for demonstrating long-term illicit drug abstinence prior to solid organ transplant [29]. Hair testing may present a complementary test in scenarios where the veracity of conventional test results is in doubt. A summary of current and future application of hair testing in precision medicine is represented in Figure 2.
\nList of current application of hair testing in personalized medicine and future diagnostic potential of omics-based approaches for precision medicine.
The human hair is capable of long term incorporation of various exo- and endogenous compounds as compared with blood plasma or urine [30]. Changes in the physicochemical properties of hair as well as genetic, environmental and hormonal factors may result in various hair disorders [31]. Hence, a good understanding of the biology and the potential use of hair as a testing substrate in medicine is a very attractive option. Hair testing approaches have been applied for drug and biomarker monitoring in forensic and toxicological screening purposes as described below:
\nVarious omics-based approaches have allowed researchers to interrogate the full complement of molecules in complex biological systems. Up to 73 species of ceramide lipids have been identified using shotgun lipidomics analysis [68, 69]. Also, there is a positive correlation between hair arsenic, sodium and iodine levels and risk of cancer development in a metallomic study [70].
\nProteomic techniques have also been successfully utilized in hair [71, 72, 73, 74, 75]. Mass spectrometry based approaches have led to the identification of peptide biomarkers of oxidative chemical damage in hair, as well as to differentiate structural damage caused by cosmetic treatment/weathering hair diseases like monilethrix [76]. Also, mass spectrometry has been used to identify 343 proteins as natural constituents of the human hair [73]. Using liquid chromatography-coupled mass spectrometry-based shotgun proteomics, ethnic-based differential proteomics signature have been identified in hair keratin protein levels [75].
\nAlso, mass spectrometry imaging (MSI) has become a major tool for tissue biomarker discovery [77]; as well as other applications such as tissue profiling in histopathology and drug distribution in forensic medicine [78]. Waki et al., utilized MSI technology to determine biomarkers for aging in the human hair cortex [79]. MSI has been shown to be able to monitor cocaine in a single strand of hair sample for forensic purposes [80]. The concentrations of cocaine varied along the shaft of the hair strand, which correspond to the chronological time the drug was ingested.
\nA protein microarray proteomics study identified autoimmunity related biomarkers for scalp lesions in alopecia areata [81]. This study identified eight potential antigen biomarkers specific for alopecia areata which were used for the design of a discriminant miniarray for alopecia areata and other scalp lesion [81]. Not least, single amino acid polymorphism (SAP) or non-synonymous single nucleotide polymorphisms (SNPs) are emerging as a potential approach for reliable human identification for forensic purposes [71, 82].
\nAn important feature of all omics-based methods is their high dependence on data repositories, algorithms and bioinformatics workflows [83]. A “public data driven” approach for integration, mining, and reuse of data would benefit the field of omics immensely [84]. Although there are individual merits for each omics based field, an integrative multi-omics method may permit a more robust molecular analysis [85]. Despite the few available papers that have addressed the use of omics-based methods in the field of dermatology [86, 87], there still remains a dearth of papers that address the application of proteomics to human hair samples for non-invasive testing of various clinical conditions for precision medicine.
\nMolecular imaging (MI) can be defined simply as the characterization and measurement of biological processes at the tissue; cellular and molecular level using optical techniques. In contrast to the ‘traditional’ diagnostic imaging, MI investigates the molecular abnormalities underlying a disease instead of the mere visualization of the end effects of the molecular aberrations [100]. Although the use of imagining in hair analysis is not new, the recent advancements have shown that the use of microscopy goes beyond the comparison and identification of microscopic characteristics in hair. More so, the combination of microscopy with spectroscopy techniques promises to be a valuable tool for the molecular characterization of hair [101, 102]. Molecular imaging of hair in comparison to other biological materials is very useful particularly in diagnosis as it provides a useful testing or screening material with minimal invasion to the subjects. Examples of imaging tools used in the analysis of hair apart from mass spectroscopy imaging include the following:
\nNanotechnology involves the control of matter at the atomic or molecular levels, i.e. at the nanoscale (1–1000 nm). Hence, nanotechnology can be defined as methods or techniques employed in the processing of materials at the atomic or sub-atomic scale to generate products with physical and chemical properties that are different to the traditional counterparts [122, 123]. Nanomedicine, on the other hand, is the application of nanotechnology in medicine or health care. Recently, it has become possible to create nanoparticles that can function in an organized manner as biological sensors for the early detection, monitoring and treatment/management of diseases [122]. Nanomedicine has provided novel ways of diagnosis and treatment of dermatological conditions. Although much of the new technologies involving the coupling of nanotechnology with other testing methods are still under investigation, it is a known fact that nanotechnology promises to generate tools and devices that would lead to an increase in the accuracy of detection and analysis of biological samples (e.g. hair). Even though, the use of nanotechnology in trichology is not prevalent, most of the current and non-invasive methods of diagnosis involving the use of nanotechnology may apply to the molecular and structural analysis of hair. Such method that mainly includes imaging and chemical analysis are often referred to as “Nanoimaging” or “Nanoanalytics” [124]. Nanoimaging, which involve the use of nanoparticles to improve the optical and spatial resolution of test materials have proved very useful in diagnosis and testing. For instance, different nanoparticles have been tested in various diagnostic applications due to some advantages such as high sensitivity and specificity, which allow for the use of a little amount biological sample [124, 125, 126].
\nHair testing can potentially contribute to weight and physical activity monitoring. Exploring this approach would provide a more measurable and objective means to assess an individual’s compliance to a reduced calorie, low carb and/or low fat dietary regimens. This is more reliable than what food frequency or PA questionnaires can accurately assess [127]. Post-translationally modified proteins, phase-I/-II enzymes, heat shock proteins, redox status proteins, etc. would be differentially expressed and may better capture dietary/PA compliance than what the classical clinical biochemical parameters (triglycerides, cholesterol, free sugar levels, etc.) would be able to do. These biochemical parameters are rather transitory and are prone to day-to-day confounding factors. For example, specific proteins may serve as excellent surrogate markers of steady-state homeostasis achieved by a more consistent compliance to a dietary or PA regimen.
\nWe have discussed various application of non-invasive hair testing in the era of precision medicine and factors that have militated against the routine use of hair testing in diagnostic pathology. Establishing a workflow that can permit the routine use of hair as a testing substrate for disease diagnosis would greatly benefit diagnostic and therapeutic aspects of precision medicine. The use of emerging omics’-based molecular approaches in hair testing would shed more light on hair-based molecular signatures for various physiological and pathological conditions.
\nHAA thanks the South African Medical Research Council (SAMRC) for a mid-career scientist research fellowship. NPK thanks the SAMRC and the National Research Foundation South African Research Chair Initiative.
\nThe Authors declare that they have no conflict of financial or non-financial interest.
Thanks to improved storage capacities, databases in various fields such as banking have grown up to a rich level. Most of the strategic sales and marketing decisions are taken by processing these data. For example, strategies such as cross-sell, up-sell, or risk management are being created as a result of processing the customer data. Because of the increasing number of customers and the need for a higher processing capacity, it has made it more difficult to identify the customer requirements in a rapid and accurate way and to present solution recommendations. Innovative data mining applications and techniques are required to solve this issue [1].
The market basket analysis is one of the data mining methods applied to identify the pattern which is found in product ownership data of customers. Thanks to this analysis, a pattern among the products frequently bought together by the customers can be established. The obtained pattern plays an active role in developing cross-sell and up-sell strategies.
Market basket analysis consists of two main processes. These are clustering and association processes, respectively. The clustering process involves grouping of similar customers in terms of clusters. Thus, those customers which should be examined in the same category will be identified. During the association process, commonness in buying behavior of customers through a selected cluster is being identified, assuming that clustered customers having similar characteristics would demonstrate similar buying behaviors.
As the banking databases have grown up to a very high volume, the association process has become a very costly process in terms of time and memory consumption. In order to improve the time and memory performance, sampling process should be included in the previous phase of association.
In this regard, a sample which involves less observations in comparison to the whole data is used. We use the term “space” to refer the whole data set. In case the representation capability of the obtained sampling is high, loss of data is minimized, and the association process is realized through the sample instead of the space itself. Thus, less data shall be processed, and association rules (ARs) shall be obtained faster by consuming less memory.
As the subject of this book chapter was focused onto the banking data, customer segmentation conducted by the bank data was accepted as the clustering. As a result of the segmentation, clusters created by similar customers were used as input of sampling.
In this chapter, sample creation methods, techniques to find ideal sampling size, the space representing capability of these samples generated by these techniques, and association rules discovered through these samples were examined, respectively. Association rules obtained from both the space and sample were used to verify the sampling process. Besides, the spared amount in terms of time consumption was calculated.
This book chapter was organized as follows: Section 2 explains the studies toward deriving association rules through the space and sampling. Section 3 explains the parameters required to obtain association rules and the Apriori algorithm. Section 4 contains parameters to create the sample, sample creation methods, and the techniques used to calculate the sample size. Section 5 examines association rules obtained from the space and the sample and the results showing the representation capability of the sample for the respective space and the results showing rewards in terms of time consumption. Section 6 gives an overview and concludes the chapter.
In association rule mining, first the item sets, which are found together frequently, are found, and then the rules are obtained from these item sets.
Association algorithms are classified according to characteristics of the obtained item sets. In early studies Agrawal-Imielinski-Swami (AIS) algorithm which was allowed to find wide item sets was used, and then algorithms were found such as Apriori, which were used frequently now and which were able to process the bigger data sets faster [2].
The mutual usage of association discovery and sample creation methods is not a new approach. Sample creation studies toward association detection have begun with papers demonstrating mathematically that it was possible to create a sample which maintained the characteristics of the space. The following studies involved several techniques calculating the optimal number of observations [3, 4, 5, 6, 7].
At the beginning of the sample size detection studies, the data to be sampled were not considered; they have tried to determine the sample size using parameters not depending on the data such as margin of error, minimum support, and minimum confidence [3]. In current studies, formulas (using variables such as maximal process length or Vapnik-Chervonenkis (VC) size of the data cluster) considering the data characteristics have appeared [4, 5, 6, 7]. There exists a number of studies focusing on how the management of metadata of big data sets are provided in a distributed computing setting [8, 9, 10, 11]. Moreover, there exists a number of studies that are conducted in the field of information systems for managing distributed data storage platforms [12, 13, 14, 15, 16]. Unlike these studies, this chapter focuses on extracting meaningful information, i.e. association rules, from the big data sets. Initial results of the experimental studies, covered in this chapter, were reported in a previous study [17]. Sections 3 and 4 give detailed information on association detection and sample creation methods, respectively, and explain the techniques used in this study.
In data mining, it is used to determine the pattern found among the association algorithms and observations [2, 18, 19]. In case any organization’s transaction database is discussed, an analogy can be established between the observations and customers and between areas where a pattern is tried to be found and the bought products. Patterns obtained by association algorithms are processed to obtain association rules.
Association rules may be defined as follows: let us call each subset of products within the database an “itemset,” and let us call each set of products purchased together by the customer a “transaction.” The support count of any itemset is defined as the number of transactions associated with the items in the set within the database. The support indicates the ratio of support count to the number of transactions within the database. The itemset which meets the minimum support requirement is called the frequent itemset (FI).
For example, if a database with 10 transactions contains product A in 3 different transactions, then the product A’s support count is 3, and its support is 0.3. In case the minimum support is defined by a value lower than 0.3, then the product A will be classified as FI.
There are several algorithms deriving FI using the transactions within the database [2, 18]. In this chapter, Apriori algorithm was preferred due its ability of deriving all itemsets within the space. This algorithm derives primarily candidate itemsets starting with one-element itemset from the database. Those providing minimum support from candidate itemsets are filtered and recorded as FI. New candidate itemsets are created from the FI obtained in the previous step by increasing the number of elements. In each step, the candidate itemsets are passed through a minimum support test, and the algorithm continues until no FI with k-elements can be generated.
Among the elements of the FI obtained from the database, it is possible to derive association rules in A- > B format. Then, AR’s support gets equal to AUB itemset support. The confidence is defined as the ratio of AUB itemset support to the A itemset support. AR should meet the minimum confidence requirement specified by the customer [2].
Assuming that A - > B rule has a support of s and the confidence of
To find out all ARs within the database, a rule mining algorithm is applied to each FI obtained. Candidate rule combinations are created for rule mining among all subsets of a selected FI in A - > B format. Those providing minimal confidence from candidate rules are filtered and recorded as association rule.
Sample creation is the process of creating a subset containing the characteristics of a data set. The subset created through sampling is expected to represent the data set (space). In traditional statistical methods, the similarity of two data sets is measured by either χ2 test or Kolmogorov–Smirnov (K-S) test.
In this study, these tests were utilized, in order to measure the similarity of the created sample in comparison with its space. A comparison was conducted through p values (the probability
Sample creation is discussed under two topics, i.e., sample creation methods and sample size determination techniques. Sample creation methods are explained in Chapter 4.1 and sample size determination techniques explained in Chapter 4.2.
When creating samples from the space, it is possible to use several sample creation methods. These methods are classified according to the selection of observations from the space. The main sample creation methods are as follows:
The observations within the space are selected without following a specific routine. The selection probability of each observation is equal.
This is used where the observations within the space can be divided into groups. The samples are created maintaining the ratio between the number of observations of groups within the space and the total number of observations. The selection probability of each observation in the same stratus is equal.
This is used where the observations within the space can be divided into groups. After the groups are determined, they are selected using the simple random sampling method. All observations within selected groups are included into the sample.
This is used where the observations within the space can be divided into groups. After the groups are determined, groups are selected by the simple random sampling method. Unlike cluster sampling, observations to be selected from groups are determined by the simple random sampling method.
Among the mentioned methods, the simple random sampling method stands up by its high speed. As the methods, which require creation of groups within the space and sorting of observations, need a pre-analysis, their time consumption is more than the simple random sampling method.
The expected parameter in sample creation methods is the size of the sample to be created. When the optimal sample size is calculated, a number which will not decrease its space representing capability should be found. Under association detection algorithms, it is important to derive all FIs and ARs within the space from the sample. In this study, techniques specialized on association detection algorithms have been examined from those developed for sample size determination [3, 4, 5, 7]. Sample size determination techniques are divided into two groups to minimize the FI and AR loss.
When the association algorithms will be run using the same parameters, support and confidence values calculated from the sample appear to be different than their counterparts calculated from the space. This margin of error is measured using two different methods. When calculating absolute margin of error, the absolute value of the difference between values from the space and the sample is considered. The relative margin of error is calculated by dividing absolute margin of error into the value within the space. In Table 1, the lines containing “absolute” at the “type of technique” column aim at reducing absolute margin of error, while those containing “relative” aim to minimize relative margin of error.
Sample size calculation techniques are provided.
Minimal sample size can be determined in terms of accuracy
All examined techniques are shown in Table 1 with suggested formulas and type of formula. The values found through the techniques determine the minimum number of transactions required for sample creation. The number of transactions which are equal to the values found is selected from the space by the preferred sample creation method.
Sample size determination techniques determine the minimum number of transactions required for sample creation. The number of transactions which are equal to the values found is selected from the space by the preferred sample creation method.
The complexity of the space is calculated theoretically using the Vapnik-Chervonenkis size [20]. Assuming that the transactions within the database are sorted according to their number of elements and that the “number of transactions” and “number of elements” are plotted on the coordinate system, the d-index value would correspond to the edge length of the largest square.
In this study, we use a d-index algorithm, which does not seek a sorting requirement among the transactions, and it calculates
The tests were performed on product ownership data of banking customers. Statistical studies’ code development was performed on the widely used R programming language.
When tests were performed, the steps below were followed:
Determine the sample size utilizing various techniques
Create three different samples for each technique using the simple random sampling method
Compare the representability of the space for the obtained sample examination with
Use the Apriori algorithm included in the
Calculate the absolute error in support and trust values, and compare the results with those obtained from the space
Compare the duration of obtaining AR and the duration of sample creation. Generate AR from the sample
Theoretically, it is expected that the samples in various sizes obtained from FI and AR results are tuned with the results from the space, that there is a correspondence between representability and absolute error, and that the duration of transactions made on the sample and the memory consumption reduce.
To accelerate the test processes, instead of 143 products of the bank, 10 different product groups were determined, and the association between those groups was examined. The utilized banking data is a matrix including 1,048,575 customers and an ownership status of customers about 10 different product groups. The lines represent customers and the columns represent product groups. In case the customer owns a product, the intersection of that line-column indicates 1, otherwise 0. In these tests the following parameters were used: accuracy
Table 2 shows varying sample sizes corresponding to varying minimum confidence values. Because γ was not used as a parameter in formulas Toivonen, Chakaravarthy FI-absolute, Chakaravarthy AR-absolute, Riondato FI-absolute, and Riondato FI-relative, there are no variations in calculated sizes.
Calculated sample sizes based on varying minimum trust values are provided.
Techniques where the calculated size is larger than the space were not used at the sample creation step.
When Table 2 was examined in detail, it is obvious that the sizes obtained from the techniques Chakaravarthy FI-absolute, Chakaravarthy AR-absolute, Riondato FI-relative, and Riondato AR-relative are larger than the space (1,048,575). As the aim was to reduce the data set, these techniques were not examined in the following tests. In order to minimize the error due to simple random sampling method, three samples were created for each of the Zaki, Toivonen, Riondato FI-absolute, and Riondato AR-absolute techniques.
Table 3 shows average
p values calculated from χ2 and K-S tests were provided based on minimum trust values.
All the techniques were found to be similar to the space.
FIs and their corresponding ARs were determined from the samples created using Apriori algorithm. To measure the similarities of FIs and ARs, absolute error was calculated through support and trust values. Zaki and Toivonen techniques were inadequate to determine all FIs and ARs existing in the space for the value γ = 0.1. Because a loss of rule was undesirable, we have observed that these two techniques were not suitable to sample creation and time consumption tests were not examined. By calculating the error by substituting incomplete values with 0, the results on Table 4 were obtained. As expected, where absolute support error was high, an also high-confidence error was found.
Average support and trust absolute error generated based on varying minimum trust values are provided.
Techniques where AR loss was experienced were not tested in terms of running time.
In a comparative review of Tables 3 and 4, no relation was detected between support and confidence errors by the results obtained from χ2 and K-S tests. We have noticed that traditional statistical measurements were inadequate in measuring the representability of the sample which was created for association mining.
In Table 5, durations until creation of AR are provided for the space and created samples. While the duration from sample creation until obtaining the AR was provided for the space, the time required for sample size determination, total average time required for sample creation, and obtaining the AR by simple random sampling method were provided for the samples. As expected, the time performance of all techniques for each value γ was found better than the space. Even more benefits are expected by using actually 143 different products instead of 10 product groups in these tests.
The time until rule mining based on varying minimum trust values γ was given in seconds.
Techniques where AR loss was experienced were not tested in terms of running time.
FI and AR results of the samples were compared to those generated from the space. Within the space, the mostly encountered depository (D) product has a ratio of 94%, credit card (CC) product has 11%, and installment loan (IL) product has 8%. These products are found together within the space by a ratio of 2.3%. In spite of this low support value, three different rules with high confidence were derived. “CC, IL → D” rule was derived, and eventually, it was observed that 92% of people who have bought CC and IL also have bought D. According to another derived rule “IL, D -> CC,” it was discovered that 31% of people who bought CC and D also bought IL. It was also found through another rule CC, D - > IL that 28% of people who bought IL and D also bought CC. These rules could not be derived from the techniques Zaki and Toivonen, and information loss was experienced. Therefore, these techniques are not suitable on sample creation for association mining. As expected, when sample size increased, the obtained absolute error in results decreased.
The utilized banking data contains information about customers and the product groups of their owned products. In other words, instead of products owned by customers, banking product groups were used. This was preferred to reduce the data set sparsity and to accelerate the test speeds. So, it was observed that even tests on the space did not take longer than 2 seconds. Whenever the advantages in terms of duration seem to be in the order of seconds, tests to be conducted with a data set containing more products (or product groups) will show decisive advantages.
Because AR mining process through the space takes a long time, we have aimed at determining a smaller sized sample representing the space and AR mining through that sample. For this purpose, in this book chapter, we have investigated for techniques which provide an ideal sample size specialized on association mining.
The samples were created using the simple random sampling method, and three different samples were obtained per technique. We have tried to prevent a potential noise in our results by creating multiple samples.
The similarity of samples to the space was measured by the χ2 test and K-S test. It was obvious that after both tests the obtained values for association mining were inadequate in measuring the representability of samples. In those tests, no relationship was found among support and confidence error values. We consider that the probability of tests giving biased outputs and the inadequacy of suggested sample sizes in measuring were the reasons for having these results.
The results indicate that the duration of AR generation within the space was compared to the total time of sample size determination, sample creation, and AR generation through the sample. It was observed that each technique was better performing in terms of space results. Riondato FI-absolute and Riondato AR-absolute techniques have given good results based on calculated absolute error values. When smaller sample size and less time consumption criteria were considered, Riondato FI-absolute technique becomes favorable.
In future studies, the data set shall be renewed in this regard, and other sample methods will also be applied. Besides, results which might be related to a single data set shall be extended with tests to be performed on another data set, and the results shall be cross-checked.
Authors are listed below with their open access chapters linked via author name:
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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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