Skip to main content

Abstract

Computational methods are designed to solve complex problems systematically and efficiently. Classification and selection procedures are often used in biological sequence and other data analysis. This chapter provides an introduction to different methods like clustering, hypothesis -testing, and classification methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Berry, D. A. (1996). Statistics: A Bayesian Perspective, Wadsworth Publishing.

    Google Scholar 

  • Birney, E., and Durbin, R. (2000). Using Gene Wise in the Drosophila annotation experiment, Genome Res 10, 547–8.

    Article  PubMed  CAS  Google Scholar 

  • Brown, M. P., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M., Jr., and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc Natl Acad Sci U S A 97, 262–7.

    Article  PubMed  CAS  Google Scholar 

  • Cai, C. Z., Han, L. Y., Ji, Z. L., and Chen, Y. Z. (2004). Enzyme family classification by support vector machines, Proteins 55, 66–76.

    Article  PubMed  CAS  Google Scholar 

  • Capriotti, E., Fariselli, P., Calabrese, R, and Casadio, R (2005). Predicting protein stability changes from sequences using support vector machines, Bioinformatics 21 Suppl 2, ii54–ii58.

    Article  PubMed  CAS  Google Scholar 

  • Coberley, C, Elashoff, M., and Mertz, L. (2004). Match/X, A gene expression pattern recognition algorithm used to identify genes which may be related to CDC2 function and cell cycle regulation, Cell Cycle 3, 804–10.

    PubMed  CAS  Google Scholar 

  • Di Cara, A., Schmidt, K., Hemmings, B. A., and Oakeley, E. J. (2005). PromoterPlot: a graphical display of promoter similarities by pattern recognition, Nucleic Acids Res 33, W423–6.

    Article  PubMed  CAS  Google Scholar 

  • Eddy, S. R (2004). What is Bayesian statistics? Nat Biotechnol 22, 1177–8.

    Article  PubMed  CAS  Google Scholar 

  • Giddings, M. C, Shah, A. A., Freier, S., Atkins, J. F., Gesteland, R F., and Matveeva, O. V. (2002). Artificial neural network prediction of antisense oligodeoxynucleotide activity, Nucleic Acids Res 30,4295–304.

    Article  PubMed  CAS  Google Scholar 

  • Hakak, Y., Walker, J. R, Li, C, Wong, W. H., Davis, K. L., Buxbaum, J. D., Haroutunian, V., and Fienberg, A. A. (2001). Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia, Proc Natl Acad Sci U S A 98, 4746–51.

    Article  PubMed  CAS  Google Scholar 

  • Hayward, G., and Davidson, V. (2003). Fuzzy logic applications, Analyst 128, 1304–6.

    Article  PubMed  CAS  Google Scholar 

  • Huesken, D., Lange, J., Mickanin, C, Weiler, J., Asselbergs, F., Warner, J., Meloon, B., Engel, S., Rosenberg, A., Cohen, D., et al. (2005). Design of a genome-wide siRNA library using an artificial neural network, Nat Biotechnol 23, 995–1001.

    Article  PubMed  CAS  Google Scholar 

  • Ibbini, M. S., and Masadeh, M. A. (2005). A fuzzy logic based closed-loop control system for blood glucose level regulation in diabetics, J Med Eng Technol 29, 64–9.

    Article  PubMed  CAS  Google Scholar 

  • Krogh, A., Larsson, B., von Heijne, G., and Sonnhammer, E. L. (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes, J Mol Biol 305, 567–80.

    Article  PubMed  CAS  Google Scholar 

  • Mathura, V. S., Schein, C. H., and Braun, W. (2003). Identifying property based sequence motifs in protein families and superfamilies: application to DNase-1 related endonucleases, Bioinformatics 19,1381–90.

    Article  PubMed  CAS  Google Scholar 

  • Oldfield, T. (2002). Pattern-recognition methods to identify secondary structure within X-ray crystallographic electron-density maps, Acta Crystallogr D Biol Crystallogr 58, 487–93.

    Article  PubMed  CAS  Google Scholar 

  • Pachter, L., and Sturmfels, B. (2004). Parametric inference for biological sequence analysis, Proc Natl Acad Sci U S A101,16138–43.

    Google Scholar 

  • Phuong, N. H., and Kreinovich, V. (2001). Fuzzy logic and its applications in medicine, Int J Med Inform 62, 165–73.

    Article  PubMed  CAS  Google Scholar 

  • Ressom, H., Reynolds, R., and Varghese, R S. (2003). Increasing the efficiency of fuzzy logic-based gene expression data analysis, Physiol Genomics 13,107–17.

    PubMed  CAS  Google Scholar 

  • Satagopan, J. M., Yandell, B. S., Newton, M. A., and Osborn, T. C. (1996). A bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo, Genetics 144, 805–16.

    PubMed  CAS  Google Scholar 

  • Schneider, I, Peltri, G., Bitterlich, N., Neu, K., Velcovsky, H. G., Morr, H., Katz, N., and Eigenbrodt, E. (2003a). Fuzzy logic-based tumor marker profiles including a new marker tumor M2-PK improved sensitivity to the detection of progression in lung cancer patients, Anticancer Res 23, 899–906.

    CAS  Google Scholar 

  • Schneider, J., Peltri, G., Bitterlich, N., Philipp, M., Velcovsky, H. G., Morr, H., Katz, N., and Eigenbrodt, E. (2003b). Fuzzy logic-based tumor marker profiles improved sensitivity of the detection of progression in small-cell lung cancer patients, Clin Exp Med 2, 185–91.

    Article  CAS  Google Scholar 

  • Seker, H., Odetayo, M. O., Petrovic, D., and Naguib, R. N. (2003). A fuzzy logic based-method for prognostic decision making in breast and prostate cancers, IEEE Trans Inf Technol Biomed 7, 114–22.

    Article  PubMed  Google Scholar 

  • Selbig, J., Mevissen, T., and Lengauer, T. (1999). Decision tree-based formation of consensus protein secondary structure prediction, Bioinformatics 75, 1039–46.

    Article  Google Scholar 

  • Senawongse, P., Dalby, A. R., and Yang, Z. R. (2005). Predicting the phosphorylation sites using hidden Markov models and machine learning methods, J Chem Inf Model 45, 1147–52.

    Article  PubMed  CAS  Google Scholar 

  • Shoemaker, J. S., Painter, I. S., and Weir, B. S. (1999). Bayesian statistics in genetics: a guide for the uninitiated, Trends Genet 75, 354–8.

    Article  Google Scholar 

  • Sinsheimer, J. S., Lake, J. A., and Little, R. J. (1996). Bayesian hypothesis testing of four- taxon topologies using molecular sequence data, Biometrics 52,193–210.

    Article  PubMed  CAS  Google Scholar 

  • States, D. J., and Botstein, D. (1991). Molecular sequence accuracy and the analysis of protein coding regions, Proc Natl Acad Sci U S A 88, 5518–22.

    Article  PubMed  CAS  Google Scholar 

  • Stolorz, P., Lapedes, A., and Xia, Y. (1992). Predicting protein secondary structure using neural net and statistical methods, J Mol Biol 225, 363–77.

    Article  PubMed  CAS  Google Scholar 

  • Suh, Y. J., Ye, K. Q., and Mendell, N. R. (2003). A method for evaluating the results of Bayesian model selection: application to linkage analyses of attributes determined by two or more genes, Hum Hered 55, 147–52.

    Article  PubMed  Google Scholar 

  • Szabo, A., Boucher, K., Carroll, W. L., Klebanov, L. B., Tsodikov, A. D., and Yakovlev, A. Y. (2002). Variable selection and pattern recognition with gene expression data generated by the microarray technology, Math Biosci 776, 71–98.

    Article  Google Scholar 

  • Taguchi, Y. H., and Oono, Y. (2005). Relational patterns of gene expression via non-metric multidimensional scaling analysis, Bioinformatics 21, 730–40.

    Article  PubMed  CAS  Google Scholar 

  • Uimari, P., Thaller, G., and Hoeschele, I. (1996). The use of multiple markers in a Bayesian method for mapping quantitative trait loci, Genetics 143, 1831–42.

    PubMed  CAS  Google Scholar 

  • Valafar, F. (2002). Pattern recognition techniques in microarray data analysis: a survey, Ann N Y Acad Sci 9S0, 41–64.

    Article  Google Scholar 

  • van Osdol, W. W., Myers, T. G., and Weinstein, J. N. (2000). Neural network techniques for informatics of cancer drug discovery, Methods Enzymol 321, 369–95.

    Article  PubMed  Google Scholar 

  • Vapnik, V. (1979). Estimation of Dependences Based on Empirical Data (in Russian). Nauka Moscow.

    Google Scholar 

  • Vapnik, V., and Chapelle, O. (2000). Bounds on error expectation for support vector machines, Neural Comput 12, 2013–36.

    Article  PubMed  CAS  Google Scholar 

  • Weinstein, J. N., Kohn, K. W., Grever, M. R., Viswanadhan, V. N., Rubinstein, L. V., Monks, A. P., Scudiero, D. A., Welch, L., Koutsoukos, A. D., Chiausa, A. J., and et al. (1992). Neural computing in cancer drug development: predicting mechanism of action, Science 258, 447–51.

    Article  PubMed  CAS  Google Scholar 

  • Yang, Z. R. (2004). Biological applications of support vector machines, Brief Bioinform 5, 328–38.

    Article  PubMed  CAS  Google Scholar 

  • Yang, Z. R., Wang, L., Young, N., Trudgian, D., and Chou, K. C. (2005). Pattern recognition methods for protein functional site prediction, Curr Protein Pept Sci 6, 479–91.

    Article  PubMed  CAS  Google Scholar 

  • Yu, X., Cao, J., Cai, Y., Shi, T., and Li, Y. (2006). Predicting rRNA-, RNA-, and DNA- binding proteins from primary structure with support vector machines, J Theor Biol 240, 175–84.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Radhakrishnan, S., Kolippakkam, D., Mathura, V.S. (2009). Introduction to Algorithms. In: Mathura, V.S., Kangueane, P. (eds) Bioinformatics: A Concept-Based Introduction. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-84870-9_3

Download citation

Publish with us

Policies and ethics