Effective components of TCM on bacterial biofilm.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"116",leadTitle:null,fullTitle:"Advances in Computer Science and Engineering",title:"Advances in Computer Science and Engineering",subtitle:null,reviewType:"peer-reviewed",abstract:"The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. 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Bacterial biofilms, a bacterium growth state, which can attach to living and non-living surfaces, consist of a small part of bacteria and self-produced hydrated matrix of extracellular polymeric substances. Biofilm bacteria are more resistant to antimicrobials compared with planktonic bacteria, which cause their elimination from food processing facing great challenges [1]. The emergence of bacterial resistance to conventional antimicrobials clearly shows that new biofilm control and removal strategies need to be proposed. Quorum sensing (QS), is an important mechanism of bacteria protection that enable bacteria to deliver special signal in response to changes of cell density in a certain environment, causing biofilm formation and other virulence factors [2]. When the bacterial population densities reach a certain threshold, bacteria will regulate virulence, biofilm formation, luminescence and etc. Virulence expression and biofilm formation, protecting bacteria from adverse environment, are considered to be harmful for pathogen therapy and human healthy life [3]. The normal operation of QS system requires the participation of signal molecules, such as acylhomoserine lactone [4]. In a word, the reagents that can inhibit QS regulation will interfere with biofilm formation, which means that the reagent possess ability of QS inhibition (QSI) [5]. As we know, traditional Chinese medicine has been applied for antibacterial and anti-inflammatory for many years. However, studies on traditional Chinese medicine for drug discoveries have focused mainly on its antibacterial property. A few attention has been given to its quorum sensing inhibition and anti-biofilm activity [6]. Thus, the ability to disrupt this signaling process and QS signals may be advantageous in the removal or prevention of bacterial biofilm. Different Chinese medicine composition shows different effects on QS [7]. In order to provide references for QSI to control biofilm formation, the effects of traditional Chinese medicines and its inhibition mechanism of biofilm formation—QS on common bacterium biofilm are reviewed in this article.
Quorum sensing, a cell-to-cell communication system, plays a key role in biofilm formation. When cell-to-cell signals arrive at a certain threshold, bacteria will secrete adhesion molecule and develop into biofilm with three dimensional structures [8]. Biofilm formation is a dynamic state which consists of (i) attachment, (ii) microcolony formation, (iii) maturation and (iv) dispersion [9, 10]. Quorum sensing affects the whole process of biofilm development. It is recognized that biofilms are mainly regulated by quorum sensing [11]. With quorum sensing response to the environment, bacterium occurs to the secretion of signaling molecules, the expression of the corresponding gene, and the secretion of extracellular polysaccharide (EPS) [12]. The study showed that the content of proteins, carbohydrates, and nucleic acids matrix increased significantly in the mature biofilm, since the biofilm matrix could protect the embedded cells from harmful conditions [13]. It has been reported that signal molecules play an initial role in QS system, and different signaling molecules are secreted by different bacterium: (i) N-Acyl homoserine lactones (AHLs), which are synthesized by LuxI-type enzyme, are mainly functioned in gram-negative bacterium, such as Aeromonas hydrophila [14] and Pseudomonas aeruginosa [15]. Most of gram-negative bacteria generate and detect several autoinducers, including C4-HSL, 3OH C4 HSL, lsovalery-HSL and etc. The chain length of AHLs can vary from 4 to 18 carbons, where oxidation status of the third carbon can change from fully reduced to fully oxidized [16]. The regulation of AHLs controls a series of target functions, such as biofilm formation, motility, fluorescence synthesis, expression of virulence genes and production of virulence factors [17]. When the concentration of signals arrives at a certain threshold, AHLs automatically enter the bacterium, binding to the cognate receptor to form an autoinducer-receptor complex, which causes the expression of functional gene (Figure 1). AHLs defective P. aeruginosa produced less virulence factors and less biofilm, and are diffusible signal molecules that may cause infections in human [16]. (ii) Gram-positive bacterium is mainly regulated by autoinducing peptides (AIPs) [18]. Since AIPs cannot pass through the cytoderm by itself, bacterium response to the environment through two-component protein, transmit the signal to the cell. The activation of the receptor kinase takes place when reaching the threshold level. The sensor kinase protein can be activated and then phosphorylate the response regulator protein, which bind to the target promoter, and subsequently transcriptional activates the genes for the two-component regulatory system, resulting in autoinduction in a dynamic range [19]. The production of AIP is expressed by relevant genes, then releasing out of extracellular membrane (Figure 1). (iii) Autoinducer-2 (AI-2), a signal molecule produced by LuxS, widely existed in both gram-positive and gram-negative bacterium. AI-2, which involved in regulating the many bacterium biofilms, was thought to be a universal signal molecule [20]. It has been demonstrated that regulation of AI-2 plays a significant role in biofilm formation in many kinds of bacteria. DPD is known to undergo intra-molecular cyclization to form distinct biologically active signal molecules, which collectively called AI-2. Thus, the AI-2 signal should not be recognized as a single structure, but a family of isomers, each bacterium representing a different mode of perception [21]. (2S, 4S)-2-methyl-2, 3, 3, 4 tetrahydroxytetrahydrofuryl borate (S-THMF-borate) is the AI-2 signal of Vibrionaceae, while S. Typhimurium produces (2R, 4S)-2-methyl-2, 3, 3, 4- tetrahydroxytetrahydrofuran (R-THMF) as AI-2 signal. Quorum sensing pathways of AI-2 differs due to different bacterial species [22]. Most of the gram-positive bacteria are sensitive to penicillin, while gram-negative bacteria are not usually influenced by penicillin. Thus, the application of QS inhibitor is of great significance in biofilm inhibition and bacteria removal [23]. The process of QS can be disrupted by different mechanisms: (i) inhibiting the production of QS signal molecules (AHL, AI-2 and AIP), (ii) reducing the activity of QS signal molecules, (iii) degradation of QS signal molecules, (iv) designing the analogues of signal molecules as QSI [24]. The regulation of QS may play a dual role on bacteria. Interferon with QS process can prevent bacteria from biofilm protection and virulence expression, but it is limited that antibiotic---antimicrobial cannot disrupt QS regulation. Traditional Chinese medicine is popular as QS inhibitors to disrupt QS signals, and thereby destroy bacterial biofilm and virulence expression without killing bacterium itself [25].
A graphic diagram of QS molecular signaling network.
Staphylococcus is one of the kinds as common and representative gram positive bacterial pathogens in the research of QS and biofilm development. Quorum-sensing regulation plays a vital role in the biofilm formation of many bacterial pathogens [26]. As previous mentioned, LuxS emzyme participated in the synthesis of AI-2, which had an indispensable impact on biofilm development of staphylococci quorum-sensing system. ΔluxS mutant strain shows more biofilm formation in vitro and enhanced virulence in Staphylococcus epidermidis of biofilm-associated infection. The inhibitors of luxS expression in vitro can be a promising QS inhibitor for the prevention of biofilm and virulence [27]. Burdock leaf ethanol fraction suppressed biofilm formation of S. aureus and Listeria monocytogenes. It was found that burdock leaf ethanol fraction (1.25 mg/ml) entirely inhibited (100%) the S. aureus biofilm formation, which was lower than MIC of the fraction. GC–MS/MS analysis shows that eight active compounds from burdock leaf fraction interfered with quorum sensing regulation and disrupted the composition of signaling molecules, thereby affecting the function of the quorum sensing system and disturbing biofilm formation. Eight active compounds should be exactly identified for real applications [28]. Later study analyzed 34% ethanol elution fraction of burdock leaf, found that 10 active compounds exhibited anti-biofilm activity, including chlorogenic acid, caffeic acid, p-coumaric acid, quercetin, ursolic acid, rutin, luteolin, crocin, benzoic acid and tenacissoside I. According to the metabolic fingerprints of burdock leaf fractions, chlorogenic acid and quercetin were demonstrated to be a potential antibiofilm of Salmonellaty phimurium compounds in burdock leaf [29]. S. aureus strains were tested for a relation between the ability of S. aureus attachment in polystyrene and the agr quorum-sensing system phenotype. Less of agr-positive strains cause biofilm formation, showing a vital impact of agr on biofilm development. Inhibitor of agr is as quorum-sensing blockers for S. aureus prevention [30]. The emergence of methicillin resistant S. aureus (MRSA) caused antibiotic invalidity and required new drugs for treating infectious diseases. Chamaecyparis obtusa essential oil had antibacterial effect against MRSA, finding agrA expression was inhibited with C. obtusa essential oil. Thus, C. obtusa essential oil regulates quorum-sensing to inhibit MRSA biofilm formation [31]. Study shows a certain concentration of baicalein (32 μg/mL and 64 μg/mL) clearly inhibited biofilm formation in vitro, and the combined use of vancomycin and baicalein generally enhance disruption of biofilms. 32 μg/mL and 64 μg/mL baicalein downregulate the quorum-sensing system regulators agrA and affecting biofilm development. Therefore, baicalein can inhibit the quorum sensing system while enhancing the permeability of vancomycin and reducing the production of staphylococcal enterotoxin A and α-hemolysin as well as inhibiting S. aureus biofilm formation. It is predicted that baicalein will be a novel drug candidate for S. aureus infections [32]. There are differences on the combination of ethanolic extracts of eight traditional Chinese medicines and four antibiotics. The ethanolic extracts of Isatis tinctoria, Scutellaria baicalensis and Rheum palmatum enhance the antimicrobial activity of four antibiotics on resistance of methicillin resistant S. aureus [33]. In summary, traditional Chinese medicine extracts inhibit QS and biofilm formation by controlling QS molecular signals, and usually regulating the cell to cell QS---AI-2. Recently, scientists deeded crude extract of anti-QS activity and Chinese herbal medicinal ingredient should be taken in-depth study in the future.
Previous studies have demonstrated that a large majority of traditional Chinese medicine show great sensitivity to gram negative bacteria QS and biofilm. It is well-known on the research of QS that Chromobacterium violaceum CV026 and P. aeruginosa PA01 is regard as two biomonitor strains to detect QSI on traditional Chinese medicine [34]. Twenty kinds of traditional Chinese medicine plants generally used in South-East Asia were screened for QS inhibition using two biomonitor strains, P. aeruginosa and C. violaceum CV026 PA01—gram negative bacteria. C. violaceum CV026, which can produce AHL signal molecule, produce purple pigment. P. aeruginosa PA01 control swarming through QS regulation. Thus, the change of purple pigment and swarming can be used to reflect the regulation of QS. This study found that 8 kinds of traditional Chinese medicine extracts possess QSI ability in C.violaceum CV026, and 4 kinds of traditional Chinese medicine extracts possess QSI ability in PA01. Lilium brownie and Panax pseudoginseng extracts possess QSI ability both in PA01 and CV026, which are meaningful for various biofilm inhibitions. The findings revealed that there are rich sources of plants on traditional Chinese medicine that contain components are able to break QS and QS-related virulence factors. However, the specific compounds and mechanism should be applied into deeper study [35]. Four organic solvents (n-hexane, DCM, methanol and 50% v/v acetone) were used to extract Ficus carica and Perilla frutescens in another study. The tests of C. violaceum CV026 and P. aeruginosa PA01 finds the extract of F. carica with dichloromethane and of P. frutescens with MeOH show the obvious inhibition of QS activity. Both of them display anti-QS ability. It is not sure the ingredient and inhibition concentration of crude extracts [36]. Study found that methanolic extract of Phyllanthus amarus interrupted the ability of C. violaceum CV026 to response towards exogenously supplied N-hexanoylhomoserine lactone, exhibiting the anti-quorum sensing activity. In addition, as the concentrations of the methanolic extracts of P. amarus increased, swarming motility, pyocyanin production and lecA: lux expressions in P. aeruginosa PA01 were reduced. Methanolic extracts of P. amarus may serve as promising anti-pathogenic drugs due to its anti-quorum sensing properties [37]. Ginseng aqueous extract at concentrations of 0.5–2.0% significantly inhibited P. aeruginosa biofilm formation. Oral administration of ginseng extracts in mice did not affect phagocytosis of a PAO1-filM mutant. According to previous study, Ginseng aqueous extract may prevent biofilm development by the regulation of QS [38]. Quorum sensing inhibitors could inhibit biofilm formation, but the exact role of quorum sensing in various stages of biofilm attachment, maturation, and dispersal is not clear. In vitro the combination of antibiotic and QS inhibitor generally lead to increase bacterial lethality, compared with treatment by an antibiotic alone. The combination of tobramycin and baicalin hydrate reduced the burkholderia cenocepacia infection. Among this study, baicalin hydrate targets the acylhomoserine lactone-based QS system present in B. cepacia complex organisms [39]. Curcumin from Curcuma longa (turmeric), an anti-QS agent, was demonstrated to inhibit the biofilm formation of E. coli, P. aeruginosa PAO1, Proteus mirabilis and Serratia marcescens, interfering with their QS systems. The treatment with curcumin may attenuate the QS-dependent factors, including exopolysaccharide production, alginate production, swimming and swarming motility of uropathogens. Curcumin is as a QSI for urinary tract treatment [40]. Three anthocyanidins (pelargonidin, cyanidin and delphinidin) decreased the formation of P. aeruginosa PAO1 biofilm at low sub-MIC (0.125 MIC). Comparing with ampicillin and streptomycin, delphinidin show the most active of anti-biofilm activity. Water-soluble delphinidin structure could be used for the design of the novel and more effective anti-biofilm agents [41]. N-acylhomoserine lactone (AHL)-based QS plays vital role in biofilm formation and virulence expression. Three Chinese herbal ingredients namely are salicylic acid, tannic acid and trans-cinnamaldehyde, are as AHL synthase inhibitors to inhibit quorum sensing. Natural products targeting AHL synthase may provide anti-QS signal synthesis for prevention of pathogenic bacteria [42]. Traditional Chinese herbs could inhibit key biofilm-associated genes in P. aeruginosa. Herba patriniae extract showed significantly reduction on the biofilm formation and change the structure of the P. aeruginosa biofilms. Further studies showed H. patriniae extract promoted its swarming motility. The possible inhibition mechanism is that H. patriniae may regulate QS to control bacterial biofilm and swarming [43]. Study found that 30 mg/ml of Melia dubia seed ethanolic extract inhibited biofilm, hemolysis and swarming motility by 92.1, 20.9, and 48.52%, suggesting that the ethanolic extract possessed potency to restrain quorum sensing of uropathogenic E. coli [5]. It has been demonstrated that quorum sensing quenching effect exists in traditional Chinese medicine plants, foreseeing the tremendous prospect of QSI application on traditional Chinese medicine [44]. The concrete mechanism of traditional Chinese herbs is unsure, but some chemical components have been found in traditional Chinese herbs. It is believed that most of traditional Chinese medicines are as promising QS inhibitors for bacterial infection and biofilm disruption. Traditional Chinese medicines may be a novel material for infectious diseases and food safety with antibiotic.
To sum up the above arguments, most researches of QSI on gram negative bacteria have been taken into deep consideration. It has been demonstrated that P. aeruginosa PA01 and C. violaceum CV026 can detect QSI of gram negative bacteria. Furthermore, the active constituent of traditional Chinese medicines also has been shown in the research. Most of active constituents are extracts of water-alcohol, since aqueous extracts are more effective on biofilm inhibition than organic solvent. Panax pseudoginseng extracts on mouse test and experiment in vitro found that aqueous extracts control biofilm formation by the regulation of QS [45]. In addition, methyl alcohol and ethyl alcohol, especially ethanol extract, are regarded as the common solvent to extract traditional Chinese medicines. Anthocyanidins, salicylic acid, baicalin and curcumin show inhibition of QS and QS-dependent biofilm, which can be assumed traditional Chinese medicines plants containing these components control QS regulation (Table 1). Now the combination of these components and antibiotic to kill bacteria are more effective that single antibiotic, so it is hopeful that the components can treat bacterial infection efficiently. Except for Staphylococcus, QSI on other gram negative bacteria is less mentioned. Ethanol extract of traditional Chinese medicines also possess strongly anti-biofilm property, but the mechanism of QS to biofilm inhibition is still unsure. Similarly, the active constituents on gram positive bacteria are flavonoid and organic acid, such as chlorogenic acid, chrysophanic acid and baicalein (Table 1). In summary, it is found that flavonoids extracted on TCM can be used as a hopeful QS inhibitor. Some of organic acid and essential oil not only reveal antibacterial property, but also show biofilm inhibition. Thus, it is believed that traditional Chinese medicines containing these components can hinder QS regulation and biofilm formation, and the plants with anti-QS and anti-biofilm property should be taken into consideration.
Type | Components of TCM | Bacterium | References |
---|---|---|---|
Flavonoids | Baicalein Baicalin Anthocyanidin Curcumin | S. aureus B. cenocepacia P. aeruginosa E. coli & P. aeruginosa | [32] [39] [41] [40] |
Organic acid | Salicylic acid Chlorogenic acid | P. aeruginosa Salmonella | [42] [29] |
Essential oil | Chamaecyparis obtusa | MRSA | [31] |
Effective components of TCM on bacterial biofilm.
Different components of traditional Chinese medicine also exert anti-QS activities on the gram-positive bacteria and gram-negative bacteria. Gram-negative bacteria seems sensitive to QS inhibitor, since C. violaceum CV026 and P. aeruginosa PA01 are usually as biomonitor strains for detection of QSI [46]. Common pathogenic bacteria, such as S. epidermidis [47], S. aureus [48], E. coli [49] and P. aeruginosa [50], are mainly interfered with biofilm development and toxicant release by QS regulation. It has been found that the combination of traditional Chinese medicine and antibiotics could improve the antibacterial activity and remove bacterial biofilm effectively. Nowadays most of traditional Chinese medicines are screened for a pathogenic bacterium QSI. However, less reports show a broad spectrum QS activity on traditional Chinese medicines [51]. Therefore, screening traditional Chinese medicine ingredients with anti-QS function, can treat the common pathogens biofilm and remove drug-resistant bacteria, being promising drugs for antibiotics auxiliary treatment. Antibiosis activity has been shown in many Chinese medicine ingredients, but now antibiotics are still the priority drugs for clinic treatment of infectious diseases. The following problems are for the better development of traditional Chinese medicine in the future. The first step is to explore QS mechanism of traditional Chinese medicine, enable traditional Chinese medicine ingredients inhibit the specific pathogens biofilm this phenomenon reach a theoretical stage thus people can have a more profound understanding. Secondly, the researches of traditional Chinese medicine still experience in a basically experimental stage. Experimental in vivo and clinical trials on traditional Chinese medicine should be strengthened, only which can lead to a further application. The last but not least, the specific efficacy of traditional Chinese medicine should be confirmed, and try to use new methods on extracting them. Since traditional Chinese medicine work usually by complex inducers, novel methods like metabolic engineering can be applied to increase the active ingredient dramatically in the meanwhile decrease the cost. It is hoped that traditional Chinese medicine could be used for food safety in the food industry.
This work has been supported by Science Research Fund of Wuhan Institute of Technology (17QD01) and the National Natural Science Foundation of China (31501582).
All authors declare that they have no conflict of interest.
The rapid development of technology has led to the registration of many processes in an electronic environment, the storage of these records, and the accessibility of these records when requested. With the evolving technology such as cloud computing, big data, the accumulation of a large amount of data stored in databases, and the process of parsing and screening useful information made data mining necessary.
It is possible to examine the data which are kept in databases and reach to huge amounts of size every second, in two parts according to their changes in time: static and temporal. Data is called the static data when its feature values do not change with time, if the feature comprise values change with time then it is called the temporal or time-series data.
Today, with the increase in processor speed and the development of storage technologies, real-world applications can easily record changing data over time.
Time-series analysis is a trend study subject because of its prevalence in various fields ranging from science, engineering, bioinformatics, finance, and government to health-care applications [1, 2, 3]. Data analysts are looking for the answers of such questions: Why does the data change this way? Are there any patterns? Which series show similar patterns? etc. Subsequence matching, indexing, anomaly detection, motif discovery, and clustering of the data are the answers of some questions [4]. Clustering, which is one of the most important concepts of data mining, defines its structure by separating unlabeled data sets into homogeneous groups. Many general-purpose clustering algorithms are used for the clustering of time-series data, either by directly or by evolving. Algorithm selection depends entirely on the purpose of the application and on the properties of the data such as sales data, exchange rates in finance, gene expression data, image data for face recognition, etc.
In the age of informatics, the analysis of multidimensional data that has emerged as part of the digital transformation in every field has gained considerable importance. These data can be from data received at different times from one or more sensors, stock data, or call records to a call center. This type of data, that is, observing the movement of a variable over time, where the results of the observation are distributed according to time, is called time-series data. Time-series analysis is used for many purposes such as future forecasts, anomaly detection, subsequence matching, clustering, motif discovery, indexing, etc. Within the scope of this study, the methods developed for the time-series data clustering which are important for every field of digital life in three main sections. In the first section, the proposed methods for the preparation of multidimensional data for clustering (dimension reduction) in the literature are categorized. In the second section, the similarity criteria to be used when deciding on the objects to be assigned to the related cluster are classified. In the third section, clustering algorithms of time-series data are examined under five main headings according to the method used. In the last part of the study, the use of time-series clustering in bioinformatics which is one of the favorite areas is included.
There are many different categorizations of time-series clustering approaches. Such as, time-series clustering approaches can be examined in three main sections according to the characteristics of the data used whether they process directly on raw data, indirectly with features extracted from the raw data, or indirectly with models built from the raw data [5]. Another category is according to the clustering method: shape-based, feature-based, and model-based [6]. But whatever the categorization is, for any time-series clustering approach, the main points to be considered are: how to measure the similarity between time series; how to compress the series or reduce dimension and what algorithm to use for cluster. Therefore, this chapter examines time-series clustering approaches according to three main building blocks: data representation methods, distance measurements, and clustering algorithms (Figure 1).
Time-series clustering.
Data representation is one of the main challenging issues for time-series clustering. Because, time-series data are much larger than memory size [7, 8] that increases the need for high processor power and time for the clustering process increases exponentially. In addition, the time-series data are multidimensional, which is a difficulty for many clustering algorithms to handle, and it slows down the calculation of the similarity measurement. Consequently, it is very important for time-series data to represent the data without slowing down the algorithm execution time and without a significant data loss. Therefore, some requirements can be listed for any data representation methods [9]:
Significantly reduce the data size/dimensionality,
Maintain the local and global shape characteristics of the time series,
Acceptable computational cost,
Reasonable level of reconstruction from the reduced representation,
Insensitivity to noise or implicit noise handling.
Dimension reduction is one of the most frequently used methods in the literature [7, 10, 11, 12] for the data representation.
The representation of a time series T with length n is a model T̅ with reduced dimensions, so that T approximates T [13]. Dimension reduction or feature extraction is a very useful method for reducing the number of variables/attributes or units in multivariate statistical analyzes so that the number of attributes can be reduced to a number that “can handle.”
Due to the noisy and high-dimensional features of many time-series data, data representations have been studied and generally examined in four main sections: data adaptive, nondata adaptive, model-based, and data dictated [6].
Data adaptive methods that have changing parameters according to processing time-series data. Methods in this category try to minimize global reconstruction error by using unequal length segments. Although it is difficult to compare several time series, this method approximates each series better. Some of the popular data adaptive representation methods are: Symbolic Aggregate Approximation (SAX) [14], Adaptive Piecewise Constant Approximation (APCA) [15], Piecewise Linear Approximation (PLA) [16], Singular Value Decomposition (SVD) [17, 18], and Symbolic Natural Language (NLG) [19].
Non-data adaptive methods are use fix-size parameters for the representing time-series data. Following methods are shown among non-data adaptive representation methods: Discrete Fourier Transform (DFT) [18], Discrete Wavelet Transform (DWT) [20, 21, 22], Discrete Cosine Transformation (DCT) [17], Perceptually Important Point (PIP) [23], Piecewise Aggregate Approximation (PAA) [24], Chebyshev Polynomials (CHEB) [25], Random Mapping [26], and Indexable Piecewise Linear Approximation (IPLA) [27].
Model-based methods assume that observed time series was produced by an underlying model. The real issue here is to find the parameters that produce this model. Two time series produced by the same set of parameters using the underlying model are considered similar. Some of the model-based methods can be listed as: Auto-regressive Moving Average (ARMA) [28, 29], Time-Series Bitmaps [30], and Hidden Markov Model (HMM) [31, 32, 33].
Data dictated methods automatically determine the dimension reduction rate but in the three methods mentioned above, the dimension reduction rates are automatically determined by the user. The most common example of data dictated method is clipped data [34, 35, 36].
Many representation methods for time-series data are proposed and each of them offering different trade-offs between the aforementioned requirements. The correct selection of the representation method plays a major role in the effectiveness and usability of the application to be performed.
In particular, the similarity measure is the most essential ingredient of time-series clustering.
The similarity or distance for the time-series clustering is approximately calculated, not based on the exact match as in traditional clustering methods. It requires to use distance function to compare two time series. In other words, the similarity of the time series is not calculated, it is estimated. If the estimated distance is large, the similarity between the time series is less and vice versa.
Similarity between two “n” sized time series T = {t1,t2,….tn} and U = {u1,u2,….un} is the length of the path connecting pair of points [11]. This distance is the measure of similarity. D (T, U) is a function that takes two times series (T, U) as input and calculates their distance “d”.
Metrics to be used in clustering must cope with the problems caused by common features of time-series data such as noise, temporal drift, longitudinal scaling, offset translation, linear drift, discontinuities, and amplitude scaling. Various methods have been developed for similarity measure, and the method to choose is problem specific. These methods can be grouped under three main headings: similarity in time, similarity in shape, and similarity in change.
The similarity between the series is that they are highly time dependent. Such a measure is costly for the raw time series, so a preprocessing or transformation is required beforehand [34, 36].
The process of separating groups according to similarities of data is called “clustering.” There are two basic principles: the similarity within the cluster is the highest and the similarity between the clusters is the least. Clustering is done on the basis of the characteristics of the data and using multivariate statistical methods. When dividing data into clusters, the similarities/distances of the data to each other are measured according to the specification of the data (discrete, continuous, nominal, ordinal, etc.)
Han and Kamber [41] classify the general-purpose clustering algorithms which are actually designed for static data in five main sections: partition-based, hierarchical-based, density-based, grid-based, and model-based. Besides these, a wide variety of algorithms has been developed for time-series data. However, some of these algorithms (ignore minor differences) intend to directly use the methods developed for static data without changing the algorithm by transforming it into a static data form from temporal data. Some approaches apply a preprocessing step on the data to be clustered before using the clustering algorithm. This preprocessing step converts the raw-time-series data into feature vectors using dimension reduction techniques, or converts them into parameters of a specified model [42].
Given a dataset on n time series T = {t1, t2,…., tn}, time-series clustering is the process of partitioning of T into C = {C1,C2,….,Ck} according to certain similarity criterion. Ci is called “cluster” where,
In this section, previously developed clustering algorithms will be categorized. Some of these algorithms work directly with raw time-series data, while others use the data presentation techniques that are previously mentioned.
Clustering algorithms are generally classified as: partitioning, hierarchical, graph-based, model-based, and density-based clustering.
The K-means [43] algorithm is a typical partition-based clustering algorithm such that the data are divided into a number of predefined sets by optimizing the predefined criteria. The most important advantage is its simplicity and speed. So it can be applied to large data sets. However, the algorithm may not produce the same result in each run and cannot handle the outlier. Self-organizing map [44] is stronger than the noisy data clustering from K-means. The user is prompted to enter the cluster number and grid sets. It is difficult to determine the number of clusters for time-series data. Other examples of partition-based clustering are CLARANS [45] and K-medoids [46]. In addition, the partitioning approach is suitable for low-dimensional, well-separated data. However, time-series data are multidimensional and often contain intersections, embedded clusters.
In essence, these algorithms act as n-dimensional vectors to time-series data and applies distance or correlation functions to determine the amount of similarity between two series. Euclidean distance, Manhattan distance, and Pearson correlation coefficient are the most commonly used functions.
Contrary to the partitioning approach, which aims segmenting data that do not intersect, the hierarchical approach produces a hierarchical series of nested clusters that can be represented graphically (dendrogram, tree-like diagram). The branches of the dendrogram show the similarity between the clusters as well as the knowledge of the shaping of the clusters. Determined number of clusters can be obtained by cutting the dendrogram at a certain level.
Hierarchical clustering methods [47, 48, 49] are based on the separating clusters into subgroups that are processed step by step as a whole, or the stepwise integration of individual clusters into a cluster [50]. Hierarchical clustering methods are divided into two methods: agglomerative clustering methods and divisive hierarchical clustering methods according to the creation of the dendrogram.
In agglomerative hierarchical clustering methods, each observation is initially treated as an independent cluster, and then repeatedly, until each individual observation obtains a single set of all observations, thereby forming a cluster with the closest observation.
In the divisive hierarchical clustering methods, initially all observations are evaluated as a single cluster and then repeatedly separated in such a way that each observation is separated from the farthest observation to form a new cluster. This process continues until all the observations create a single cluster.
Hierarchical clustering not only forms a group of similar series but also provides a graphical representation of the data. Graphical presentation allows the user to have an overall view of the data and an idea of data distribution. However, a small change in the data set leads to large changes in the hierarchical dendrogram. Another drawback is high computational complexity.
The density-based clustering approach is based on the concepts of density and attraction of objects. The idea is to create clusters of dense multi-dimensional areas where objects attract each other. In the core of dense areas, objects are very close together and crowded. The objects in the walls of the clusters were scattered less frequently than the core. In other words, density-based clustering determines dense areas of object space. The clusters are dense areas which are separated by rare dense areas. DBSCAN [51] and OPTICS [52] algorithms are the most known of density-based clustering examples.
The density-based approach is robust for noisy environments. The method also deals with outliers when defining embedded clusters. However, density-based clustering techniques cause difficulties due to high computational complexity and input parameter dependency when the dimensional index structure is not used.
The model-based approach [53, 54, 55] uses a statistical infrastructure to model the cluster structure of the time-series data. It is assumed that the underlying probability distributions of the data come from the final mixture. Model-based algorithms usually try to estimate the likelihood of the model parameters by applying some statistical techniques such as Expectation Maximization (EM). The EM algorithm iterates between an “E-step,” which computes a matrix z such that zik is an estimate of the conditional probability that observation i belongs to group k given the current parameter estimates, and an “M-step,” which computes maximum likelihood parameter estimates given z. Each data object is assigned to a cluster with the highest probability until the EM algorithm converges, so as to maximize likelihood for the entirety of the grant.
The most important advantage of the model-based approach is to estimate the probability that i. observation belongs to k. cluster. In some cases, the time series is likely to belong to more than one cluster. For such time-series data, the probability-giving function of the approach is the reason for preference. In this approach, it is assumed that the data set has a certain distribution but this assumption is not always correct.
In this approach, grids made up of square cells are used to examine the data space. It is independent of the number of objects in the database due to the used grid structure. The most typical example is STING [56], which uses various levels of quadrilateral cells at different levels of resolution. It precalculates and records statistical information about the properties of each cell. The query process usually begins with a high-level hierarchical structure. For each cell at the current level, the confidence interval, which reflects the cell’s query relation, is computed. Unrelated cells are exempt from the next steps. The query process continues for the corresponding cells in the lower level until reaching the lowest layer.
After analyzing the data set and obtaining the clustering solution, there is no guarantee of the significance and reliability of the results. The data will be clustered even if there is no natural grouping. Therefore, whether the clustering solution obtained is different from the random solution should be determined by applying some tests. Some methods developed to test the quality of clustering solutions are classified into two types: external index and internal index.
The external index is the most commonly used clustering evaluation method also known as external validation, external criterion. The ground truth is the goal clusters, usually created by experts. This index measures how well the target clusters and the resulting clusters overlap. Entropy, Adjusted Rand Index (ARI), F-measure, Jaccard Score, Fowlkes and Mallows Index (FM), and Cluster Similarity Measure (CSM) are the most known external indexes.
The internal indexes evaluate clustering results using the features of data sets and meta-data without any external information. These are often used in cases where the correct solutions are not known. Sum of squared error is one of the most used internal methods which the distance to the nearest cluster determines the error. So clusters with similar time series are expected to give lower error values. Distance between two clusters (CD) index, root-mean-square standard deviation (RMSSTD), Silhouette index, R-squared index, Hubert-Levin index, semi-partial R-squared (SPR) index, weighted inter-intra index, homogeneity index, and separation index are the common internal indexes.
The funFEM algorithm [55, 57] allows to cluster time series or, more generally, functional data. FunFem is based on a discriminative functional mixture model (DFM) which allows the clustering of the curves (data) in a functional subspace. If the observed curves are {
an E step in which posterior probabilities that observations belong to the K groups are computed,
an F step that estimates the orientation matrix U of the discriminative latent space conditionally to the posterior probabilities,
an M step in which parameters of the mixture model are estimated in the latent subspace by maximizing the conditional expectation of the complete likelihood.
Fisher-EM algorithm updates the parameters repeatedly until the Aitken criterion is provided. Aitken criterion estimates the asymptotic maximum of the log-likelihood in order to detect in advance the algorithm converge [57]. In model-based clustering, a model is defined by its number of component/cluster K and its parameterization. In model selection task, several models are reviewed while selecting the most appropriate model for the considered data.
FunFEM allows to choose between AIC (Akaike Information Criterion) [58], BIC (Bayesian information criteria) [59], and ICL (Integrated Completed Likelihood) [60] when deciding the number of clusters. The penalty terms are:
FunFem is implemented in R programming languages and serves as a function [61]. The algorithm is applied on a time series gene expression data in the following section. Input of the algorithm is gene expression data which is given in Table 1. The table shows the gene expression values measured as a result of the microarray experiment. The measurement was performed at six different times for each gene. The data were taken from the GEO database (GSE2241) [62]. FunFEM method is decided, and the best model is DkBk with K = 4 (bic = −152654.5) for input data. As a result, method assigned each gene to the appropriate cluster which is determined by the algorithm. Table 2 demonstrates the gene symbol and cluster number. As a result, method assigned each gene to the appropriate cluster which is determined by the algorithm (Table 2).
Gene Symbol | TP1 | TP2 | TP3 | TP4 | TP5 | TP6 |
---|---|---|---|---|---|---|
AADAC | 18.4 | 29.7 | 30 | 79.7 | 86.7 | 163.2 |
AAK1 | 253.2 | 141.8 | 49.2 | 118.7 | 145.2 | 126.7 |
AAMP | 490 | 340.9 | 109.1 | 198.4 | 210.5 | 212 |
AANAT | 5.6 | 1.4 | 3.7 | 3.1 | 1.6 | 4.9 |
AARS | 1770 | 793.6 | 226.5 | 1008.9 | 713.3 | 1253.7 |
AASDHPPT | 940.1 | 570.5 | 167.2 | 268.6 | 683 | 263.5 |
AASS | 10.9 | 1.9 | 1.5 | 4.1 | 19.7 | 25.5 |
AATF | 543.4 | 520.1 | 114.5 | 305.7 | 354.2 | 384.9 |
AATK | 124.5 | 74.5 | 17 | 25.6 | 64.6 | 13.6 |
. | . | . | . | . | . | . |
. | . | . | . | . | . | . |
ZP2 | 4.1 | 1.4 | 0.8 | 1.4 | 1.4 | 3 |
ZPBP | 23.4 | 13.7 | 7 | 7.8 | 22.3 | 26.9 |
ZW10 | 517.1 | 374.5 | 72.6 | 240.8 | 345.7 | 333.1 |
ZWINT | 1245.4 | 983.4 | 495.3 | 597.4 | 1074.3 | 620.7 |
ZYX | 721.6 | 554.9 | 135.5 | 631.5 | 330.9 | 706.8 |
ZZEF1 | 90.5 | 49.3 | 18.6 | 66.7 | 10.4 | 52.2 |
ZZZ3 | 457.3 | 317.1 | 93 | 243.2 | 657.5 | 443 |
Input data of the FunFEM algorithm.
Gene symbol | Cluster number |
---|---|
AADAC | 2 |
AAK1 | 3 |
AAMP | 3 |
AANAT | 1 |
AARS | 4 |
AASDHPPT | 3 |
AASS | 1 |
AATF | 3 |
AATK | 2 |
. | . |
. | . |
ZP2 | 1 |
ZPBP | 1 |
ZW10 | 3 |
ZWINT | 4 |
ZYX | 4 |
ZZEF1 | 2 |
ZZZ3 | 3 |
Output data of the FunFEM algorithm.
The approach to be taken depends on the application area and the characteristics of the data. For this reason, as a case study, the clustering of gene expression data, which is a special area of clustering of time-series data, will be examined in this section. Microarray is the technology which measures the expression levels of large numbers of genes simultaneously. DNA microarray technology overcomes traditional approaches in the identification of gene copies in a genome, in the identification of nucleotide polymorphisms and mutations, and in the discovery and development of new drugs. It is used as a diagnostic tool for diseases. DNA microarrays are widely used to classify gene expression changes in cancer cells.
The gene expression time series (gene profile) is a set of data generated by measuring expression levels at different cases/times in a single sample. Gene expression time series have two main characteristics, short and unevenly sampled. In The Stanford Microarray database, more than 80% of the time-series experiments contains less than 9 time points [63]. Observations below 50 are considered to be quite short for statistical analysis. Gene expression time-series data are separated from other time-series data by this characteristics (business, finance, etc.). In addition to these characteristics, three basic similarity requirements can be identified for the gene expression time series: scaling and shifting, unevenly distributed sampling points, and shape (internal structure) [64]. Scaling and shifting problems arise due to two reasons: (i) the expression of genes with a common sequence is similar, but in this case, the genes need not have the same level of expression at the same time. (ii) Microarray technology, which is often corrected by normalization. The scaling and shifting factor in the expression level may hide similar expressions and should not be taken into account when measuring the similarity between the two expression profiles. Sampling interval length is informative and cannot be ignored in similarity comparisons. In microarray experiments, the density change characterizes the shape of the expression profile rather than the density of the gene expression. The internal structure can be represented by deterministic function, symbols describing the series, or statistical models.
There are many popular clustering techniques for gene expression data. The common goal of all is to explain the different functional roles of the genes that play a key biological process. Genes expressed in a similar way may have a similar functional role in the process [65].
In addition to all these approaches, it is possible to examine the cluster of gene expression data in three different classes as gene-based clustering, sample-based clustering, and subspace clustering (Figure 2) [66]. In gene-based clustering, genes are treated as objects, instances (time-point/patient-intact) as features. Sample-based clustering is exactly the opposite: samples are treated as objects, genes as features. The distinction between these two clustering approaches is based on the basic characterization of the clustering process used for gene expression data. Some clustering algorithms, such as K-means and hierarchical approach, can be used to cluster both genes and fragments of samples. In the molecular biology, “any function in the cell is carried out with the participation of a small subset of genes, and the cellular function only occurs on a small sample subset.” With this idea, genes and samples are handled symmetrically in subspace clustering; gene or sample, object or features.
Gene expression data clustering approaches.
In gene-based clustering, the aim is to group the co-expressed genes together. However, due to the complex nature of microarray experiments, gene expression data often contain high amounts of noise, characterizing features such as gene expression data often linked to each other (clusters often have a high intersection ratio), and some problems arising from constraints from the biological domain.
Also, among biologists who will use microarray data, the relationship between genes or clusters that are usually related to each other within the cluster, rather than the clusters of genes, is a more favorite subject. That is, it is also important for the algorithm to make graphical presentations not just clusters. K-means, self-organizing maps (SOM), hierarchical clustering, graph-theoretic approach, model-based clustering, and density-based approach (DHC) are the examples of gene-based clustering algorithms.
The goal of the sample-based approach is to find the phenotype structure or the sub-structure of the sample. The phenotypes of the samples studied [67] can only be distinguished by small gene subsets whose expression levels are highly correlated with cluster discrimination. These genes are called informative genes. Other genes in the expression matrix have no role in the decomposition of the samples and are considered noise in the database. Traditional clustering algorithms, such as K-means, SOM, and hierarchical clustering, can be applied directly to clustering samples taking all genes as features. The ratio of the promoter genes to the nonrelated genes (noise ratio) is usually 1:10. This also hinders the reliability of the clustering algorithm. These methods are used to identify the informative genes. Selection of the informative genes is examined in two different categories as supervised and unsupervised. The supervised approach is used in cases where phenotype information such as “patient” and “healthy” is added. In this example, the classifier containing only the informative genes is constructed using this information. The supervised approach is often used by biologists to identify informative genes. In the unsupervised approach, no label specifying the phenotype of the samples is placed. The lack of labeling and therefore the fact that the informative genes do not guide clustering makes the unsupervised approach more complicated. There are two problems that need to be addressed in the unsupervised approach: (i) the high number of genes versus the limited number of samples and (ii) the vast majority of collected genes are irrelevant. Two strategies can be mentioned for these problems in the unsupervised approach: unsupervised gene selection and clustering. In unsupervised gene selection, gene selection and sample clustering are treated as two separate processes. First, the gene size is reduced, and then classical clustering algorithms are applied. Since there is no training set, the choice of gene is based solely on statistical models that analyze the variance of gene expression data. Associated clustering dynamically supports the combination of repetitive clustering and gene selection processes by the use of the relationship between genes and samples. After many repetitions, the sample fragments converge to the real sample structure and the selected genes are likely candidates for the informative gene cluster.
When subspace clustering is applied to gene expression vectors, it is treated as a “block” consisting of clusters of genes and subclasses of experimental conditions. The expression pattern of the genes in the same block is consistent under the condition in that block. Different greedy heuristic approaches have been adapted to approximate optimal solution.
Subspace clustering was first described by Agrawal et al. in 1998 on general data mining [68]. In subspace clustering, two subspace sets may share the same objects and properties, while some objects may not belong to any subspace set. Subspace clustering methods usually define a model to determine the target block and then search in the gen-sample space. Some examples of subspatial cluster methods proposed for gene expression are biclustering [69], coupled two way clustering (CTWC) [70], and plaid model [71].
According to different clustering criteria, data can be clustered such as the co-expressing gene groups, the samples belonging to the same phenotype or genes from the same biological process. However, even if the same criteria are used in different clustering algorithms, the data can be clustered in different forms. For this reason, it is necessary to select more suitable algorithm for data distribution.
Clustering for time-series data is used as an effective method for data analysis of many areas from social media usage and financial data to bioinformatics. There are various methods introduced for time-series data. Which approach is chosen is specific to the application. The application is determined by the needs such as time, speed, reliability, storage, and so on. When determining the approach to clustering, three basic issues need to be decided: data representation, similarity measure, and clustering algorithm.
The data representation involves transforming the multi-dimensional and noisy structure of the time-series data into a less dimensional that best expresses the whole data. The most commonly used method for this purpose is dimension reduction or feature extraction.
It is challenging to measure the similarity of two time series. The chapter has been examined similarity measures in three sections as similarity in shape, similarity in time, and similarity in change.
For the time-series clustering algorithms, it is not wrong to say that the evolution of conventional clustering algorithms. Therefore, the classification of traditional clustering algorithms (developed for static data) has been included. It is classified as partitioning, hierarchical, model-based, grid-based, and density-based. Partition algorithms initially require prototypes. The accuracy of the algorithm depends on the defined prototype and updated method. However, they are successful in finding similar series and clustering time series with equal length. The fact that the number of clusters is not given as the initial parameter is a prominent and well-known feature of hierarchical algorithms. At the same time, works on time series that are not of equal length causes it to be one step ahead of other algorithms. However, hierarchical algorithms are not suitable for large data sets due to the complexity of the calculation and the scalability problem. Model-based algorithms suffer from problems such as initialization of parameters based on user predictions and slow processing time for large databases. Density-based algorithms are not generally preferred over time-series data due to their high working complexity. Each approach has pros and cons compared to each other, and the choice of algorithm for time-series clustering varies completely according to the characteristics of the data and the needs of the application. Therefore, in the last chapter, a study on the clustering of gene expression data, which is a specific field of application, has been mentioned.
In time-series data clustering, there is a need for algorithms that execute fast, accurate, and with less memory on large data sets that can meet today’s needs.
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