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Machine Learning with Special Emphasis on Support Vector Machines (SVMs) in Systems Biology: A Plant Perspective

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Agricultural Bioinformatics

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Abstract

Systems biology has been progressing with integrative genomics and tools such as bioinformatics. Recent developments in high-throughput techniques have led to the accumulation of deluge of biological data. To address specific biological questions and to generate biologically meaningful information from this deluge of data, there was a need to integrate components and system levels at biological point of view. Combined strategies from systems biology and computational biology lead to computational systems biology. Logical applications from machine learning have lots of applications with state-of-the-art techniques to deal with this data. Machine-learning applications in biology gave enhancements to the overall aspects of biological problems and their fast and accurate solutions. This chapter addresses the implications and applications of machine-learning techniques with special emphasis on support vector machines, on plants and associated research areas.

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Abbreviations

ANN:

Artificial neural network

ATF3:

Activating transcription factor 3

IFN γ:

Interferon gamma

LOO:

Leave-one-out

MCMV:

Murine cytomegalovirus

miRNA:

microRNA

SNPs:

Single nucleotide polymorphisms

SVMs:

Support Vector Machines

TRN:

Transcriptional regulatory network

References

  • Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach. MIT Press, Cambridge

    Google Scholar 

  • Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16:412–424

    Article  CAS  PubMed  Google Scholar 

  • Brown MPS, Grundy WN, Lion D, Cristianini N, Sugnet CW, Furey TS, Ares M Jr, Haussler D (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 97:262–297

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Bruggeman FJ, Westerhoff HV (2007) The nature of systems biology. Trends Microbiol 15:45–50

    Article  CAS  PubMed  Google Scholar 

  • Bülow L, Schindler M, Choi C, Hehl R (2004) PathoPlant: a database on plant-pathogen interactions. In Silico Biol 4:0044

    Google Scholar 

  • Cui D, Zhang O, Li M, Zhao Y, Hartman GL (2009) Detection of soybean rust using a multispectral image sensor. Sens Instrum Food Qual 3:49–56

    Article  Google Scholar 

  • De Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103

    Article  PubMed  Google Scholar 

  • Donnes P, Elofsson A (2002) Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 3:25

    Article  PubMed Central  PubMed  Google Scholar 

  • Fiehn O (2002) Metabolomics-the link between genotypes and phenotypes. Plant Mol Biol 48:155–171

    Article  CAS  PubMed  Google Scholar 

  • Garg A, Bhasin M, Raghava GPS (2005) Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search. J Biol Chem 280:14427–14432

    Article  CAS  PubMed  Google Scholar 

  • Gkirtzou K, Tsamardinos L, Tsakalides P, Poirazi P (2010) Mature Bayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One 5:e11843

    Article  PubMed Central  PubMed  Google Scholar 

  • Gupta A, Singh TR (2013) SHIFT: server for hidden stops analysis in frame-shifted translation. BMC Res Notes 6:68

    Article  PubMed Central  PubMed  Google Scholar 

  • Han X, Gross RW (2003) Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J Lipid Res 4:1071–1079

    Article  Google Scholar 

  • Huang S (2004) Back to the biology in systems biology: what can we learn from biomolecular networks? Brief Funct Genomic Proteomic 2:279–297

    Article  CAS  PubMed  Google Scholar 

  • Huang S, Wikswo J (2006) Dimensions of systems biology. Rev Physiol Biochem Pharmacol 157:81–104

    Article  CAS  PubMed  Google Scholar 

  • Jiang P, Wu H, Wang W, Ma W, Sun X et al (2007) MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 35:339–344

    Article  Google Scholar 

  • Joachims T (1999) Making large-scale SVM learning practical. In: Scholkopf B, Burges C, Smole A (eds) Advances in kernel methods – support vector learning. MIT Press, Cambridge, pp 169–184

    Google Scholar 

  • Kaundal R, Kapoor AS, Raghava GPS (2006) Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics 7:485

    Article  PubMed Central  PubMed  Google Scholar 

  • Kenyon C (2005) The plasticity of aging: insight from long lived mutant. Cell 120:449–460

    Article  CAS  PubMed  Google Scholar 

  • Kim SK, Nam JW, Rhee JK, Lee WJ, Zhang BT (2006) miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics 7:411

    Article  PubMed Central  PubMed  Google Scholar 

  • Klipp E, Heinrich R et al (2002) Prediction of temporal gene expression. Metabolic optimization by re-distribution of enzyme activities. Eur J Biochem 269:5604–5613

    Article  Google Scholar 

  • Laska MS, Wootton JT (1998) Theoretical concepts and empirical approaches to measuring interaction strength. Ecology 79:461–476

    Article  Google Scholar 

  • Li P, Zang W, Li Y, Xu F, Wang J, Shi T (2011) AtPID: the overall hierarchical functional protein interaction network interface and analytic platform for Arabidopsis. Nucl Acids Res 39(suppl 1):D1130–D1133

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Liang Y, Zhang F, Wang J, Joshi T, Wang Y et al (2011) Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE. PLoS One 6:e21750

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Longo VD, Finch CE (2003) Evolutionary medicine: from dwarf model systems to healthy centenarians. Science 299:1342–1346

    Article  PubMed  Google Scholar 

  • Longo VD, Leiber MR, Vijg J (2008) Turning antiaging genes against cancer. Mol Cell Biol 9:903–910

    CAS  Google Scholar 

  • Man Q-K, Zheng C-H, Wang X-F, Lin F-Y (2008) Recognition of plant leaves using support vector machine. Commun Comput Inf Sci 15:192–199

    Article  Google Scholar 

  • Matukumalli LK, Grefenstette JJ, Hyten DL, Choi I-Y, Cregan PB, Tassell CPV (2006) Application of machine learning in SNP discovery. BMC Bioinformatics 7:4

    Article  PubMed Central  PubMed  Google Scholar 

  • Morel NM, Holland JM, van der Greef J, Marple EW et al (2004) Primer on medical genomics. Part XIV: Introduction to systems biology-a new approach to understanding disease and treatment. Mayo Clin Proc 79:651–658

    Article  CAS  PubMed  Google Scholar 

  • Nelander S, Wang W, Nilsson B, Pratilas C, She QB, Rossen N, Gennemark P (2008) Models from experiments: combinatorial drug perturbations of cancer cells. Mol Syst Biol 4:216

    Article  PubMed Central  PubMed  Google Scholar 

  • Ng SK, Zhang Z, Tan SH (2003) Integrative approach for computationally inferring protein domain interactions. Bioinformatics 19:923–929

    Article  CAS  PubMed  Google Scholar 

  • Olivier BG, Snoep JL (2004) Web-based kinetic modeling using JWS online. Bioinformatics 20:2143–2144

    Article  CAS  PubMed  Google Scholar 

  • Robertson SH, Smith CK, Langhans AL, McLinden SE, Oberhardt MA, Jakab KR, Dzamba B, DeSimone DW, Papin JA, Peirce SM (2007) Multiscale computational analysis of Xenopus laevis morphogenesis reveals key insights of systems level behaviour. BMC Syst Biol 1:46

    Article  PubMed Central  PubMed  Google Scholar 

  • Rosenberger CM, Clark AE, Treuting PM, Jhonson CD, Aderem A (2008) Atf3 regulates mcmv infection in mice by modulating inf γ expression in natural killer cells. Proc Natl Acad Sci U S A 105:2544–2549

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Shu O, Robin BC (2004) The TIGR Plant Repeat Databases: a collective resource for the identification of repetitive sequences in plants. Nucleic Acids Res 32:D360–D363

    Article  Google Scholar 

  • Singh TR (2011) Phylogenetic networks: concepts, algorithms and applications, book review. Curr Sci 100:1570–1571

    Google Scholar 

  • Singh TR, Gupta A, Riju A, Mahalaxmi M, Seal A, Arunachalam V (2011) Computational identification and analysis of single nucleotide polymorphisms and insertions/deletions in expressed sequence tag data of Eucalyptus. J Genet 90:e34–e38

    Article  PubMed  Google Scholar 

  • Tang, YH, Baojun Y (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: Proceedings of international conference on engineering computation. IEEE Computer Society, pp 79–83

    Google Scholar 

  • Tong AH, Lesage G, Bader GD, Ding H, Xu H et al (2004) Global mapping of the yeast genetic interaction network. Science 294:2364–2368

    Article  Google Scholar 

  • Wang Y, Jin C, Zhou M, Zhou A (2012) An SVM-based approach to discover microRNA precursors in plant genomes. Lect Notes Comput Sci 7104:304–315

    Article  Google Scholar 

  • Ward JJ, McGuffin LJ, Buxton BF, Jones DT (2003) Secondary structure prediction using support vector machines. Bioinformatics 19:1650–1655

    Article  CAS  PubMed  Google Scholar 

  • Winnenburg R, Baldwin TK, Urban M, Rawlings C, Köhler J, Hammond-Kosack KE (2006) PHI-base: a new database for pathogen host interactions. Nucleic Acids Res 34:D459–D464

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Wu Y, Wei B, Liu H, Li T, Rayner S (2011) MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 12:107

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Xuan P, Guo M, Huang Y, Li W, Huang Y (2011) MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs. PLoS One 6:e27422

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Yang ZR (2004) Biological applications of support vector machines. Brief Bioinformatics 5:328–338

    Article  CAS  PubMed  Google Scholar 

  • Yang Y, Wang Y-P, Li K-B (2008) MiRTif: a support vector machine-based microRNA target interaction filter. BMC Bioinformatics 9:S4

    Article  PubMed Central  PubMed  Google Scholar 

  • Yin-xiao MA, Min YAO (2007) Application of SVM in plant classification. Bull Sci Technol 3:404–407

    Google Scholar 

  • Zhang L, Athale CA, Deisboeck TS (2007) Development of a three dimensional multiscale agent based tumor model: simulating gene protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. J Theor Biol 244:96–107

    Article  CAS  PubMed  Google Scholar 

  • Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW (2008) Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data. BMC Bioinformatics 9:203

    Article  PubMed Central  PubMed  Google Scholar 

  • Zhao X-M, Zhang X-W, Tang W-H, Chen L (2009) FPPI: Fusarium graminearum protein-protein interaction database. J Proteome Res 8:4714–4721

    Article  CAS  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Waknaghat, Solan, HP, India

    Tiratha Raj Singh

Authors
  1. Tiratha Raj Singh
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Corresponding author

Correspondence to Tiratha Raj Singh .

Editor information

Editors and Affiliations

  1. Department of Genetics, Osmania University, Hyderabad, Andhra Pradesh, India

    Kavi Kishor P.B.

  2. Department of Biotechnology, Birla Institute of Technology, Ranchi, Jharkhand, India

    Rajib Bandopadhyay

  3. Bioclues Organization, IKP Knowledge Park, Picket, Secunderabad, Andhra Pradesh, India

    Prashanth Suravajhala

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Singh, T.R. (2014). Machine Learning with Special Emphasis on Support Vector Machines (SVMs) in Systems Biology: A Plant Perspective. In: P.B., K., Bandopadhyay, R., Suravajhala, P. (eds) Agricultural Bioinformatics. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1880-7_16

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