Skip to main content
SpringerLink
Log in

Neurocomputing for Certain Bioinformatics Tasks

  • Conference paper
Monitoring, Security, and Rescue Techniques in Multiagent Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 28))

  • 534 Accesses

Summary

Different bioinformatics tasks like gene sequence analysis, gene finding, protein structure prediction and analysis, gene expression with microarray analysis and gene regulatory network analysis are described along with some classical approaches. The relevance of intelligent systems and neural networks to these problems is mentioned. Different neural network based algorithms to address the aforesaid tasks are then presented. Finally some limitations of the current research activity are provided. An extensive bibliography is included.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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

  1. Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge, MA (1998)

    Google Scholar 

  2. Altman, R.B., Valencia, A., Miyano, S., Ranganathan, S.: Challenges for intelligent systems in biology. IEEE Intelligent Systems 16 (2001) 14–20

    Article  Google Scholar 

  3. setubal, J., Meidanis, J.: Introduction to Computational Molecular Biology. International Thomson Publishing, 20 park plaza, Boston, MA 02116 (1999)

    Google Scholar 

  4. Nash, H., Blair, D., Grefenstette, J.: Comparing algorithms for large-scale sequence analysis. Proc. 2nd IEEE International Symposium on Bioinformatics and Bioengineering (BIBE’01) (2001) 89–96

    Google Scholar 

  5. Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48 (1970) 443–453

    Article  Google Scholar 

  6. Smith, T.F., Waterman, M.S.: Identification of common molecular sequences. Journal of Molecular Biology 147 (1981) 195–197

    Article  Google Scholar 

  7. Fickett, J.W.: Finding genes by computer: The state of the art. Trends in Genetics 12 (1996) 316–320

    Article  Google Scholar 

  8. Salzberg, S.L., Searls, D.B., Kasif, S.: Computational Methods In Molecular Biology. Elsevier Science, Amsterdam (1998)

    MATH  Google Scholar 

  9. Chou, P., Fasmann, G.: Prediction of the secondary structure of proteins from their amino acid sequence. Advances in Enzymology 47 (1978) 145–148

    Google Scholar 

  10. Luscombe, N.M., Greenbaum, D., Gerstein, M.: What is Bioinformatics? A Proposed Definition and Overview of the Field. Yearbook of Medical Informatics (2001) 83–100

    Google Scholar 

  11. Quackenbush, J.: Computational analysis of microarray data. National Review of Genetics 2 (2001) 418–427

    Article  Google Scholar 

  12. Allex, C.F., Shavlik, J.W., Blattner, F.R.: Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies. Bioinformatics 15 (1999) 723–728

    Article  Google Scholar 

  13. Jones, D.T.: GenTHREADER: An Efficient and Reliable Protein Fold Recognition. Journal of Molecular Biology 287 (1999) 797–815

    Article  Google Scholar 

  14. Arakawa, M., Hasegawa, K., Funatsu, K.: Application of the novel molecular alignment method using the Hopfield Neural Network to 3D-QSAR. J Chem Inf Comput Sci. 43 (2003) 1396–1402

    Article  Google Scholar 

  15. Hirst, J.D., Sternberg, M.J.: Prediction of structural and functional features of protein and nucleic acid sequences by artificial neural networks. Biochemistry 31 (1992) 7211–7218

    Article  Google Scholar 

  16. Petersen, S.B., Bohr, H., Bohr, J., Brunak, S., Cotterill, R.M., Fredholm, H., Lautrup, B.: Training neural networks to analyse biological sequences. Trends Biotechnol. 8 (1990) 304–308

    Article  Google Scholar 

  17. Cai, Y., Chen, C: Artificial neural network method for discriminating coding regions of eukaryotic genes. Comput Appl Biosci. 11 (1995) 497–501

    Google Scholar 

  18. Sun, J., Song, W.Y., Zhu, L.H., Chen, R.S.: Analysis of tRNA gene sequences by neural network. J Comput Biol. 2 (1995) 409–416

    Article  Google Scholar 

  19. Lukashin, A.V., Anshelevich, V.V., Amirikyan, B.R., Gragerov, A.I., Frank-Kamenetskii, M.D.: Neural network models for promoter recognition. J Biomol Struct Dyn. 6 (1989) 1123–1133

    Google Scholar 

  20. Kalate, R.N., Tambe, S.S., Kulkarni, B.D.: Artificial neural networks for prediction of mycobacterial promoter sequences. Comput Biol Chem. 27 (2003) 555–564

    Article  Google Scholar 

  21. Sherriff, A., Ott, J.: Applications of neural networks for gene finding. Adv Genet. 42 (2001) 287–297

    Article  Google Scholar 

  22. Reese, M.G.: Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Comput Chem. 26 (2001) 51–56

    Article  Google Scholar 

  23. Mahadevan, I., Ghosh, I.: Analysis of E.coli promoter structures using neural networks. Nucleic Acids Res. 22 (1994) 2158–2165

    Google Scholar 

  24. Qian, N., Sejnowski, T.J.: Predicting the secondary structure of globular proteins using neural network models. Journal Molecular Biology 202 (1988) 865–884

    Article  Google Scholar 

  25. Rost, B., Sander, C: Improved prediction of protein secondary structure by use of sequence profiles and neural networks. Proc. National Academy of Sciences USA 90 (1993) 7558–7562

    Article  Google Scholar 

  26. Rost, B., Sander, C: Prediction of protein secondary structure at better than 70% accuracy. Journal of Molecular Biology 232 (1993) 584–599

    Article  Google Scholar 

  27. Kaur, H., Raghava, G.P.: A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics. 2004 May 14 (2004) accepted

    Google Scholar 

  28. McGuffin LJ, Bryson K, J.D.: The PSIPRED protein structure prediction server. Bioinformatics 16 (2000) 404–405

    Article  Google Scholar 

  29. Hirst, M.J.W.J.D.: Predicting protein secondary structure by cascade-correlation neural networks. Bioinformatics 20 (2004) 419–420

    Article  Google Scholar 

  30. Pasquier, C, Promponas, V.J., Hamodrakas, S.J.: PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications. Proteins 44 (2001) 361–369

    Article  Google Scholar 

  31. Ding, C.H., Dubchak, L: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17 (2001) 349–358

    Article  Google Scholar 

  32. Berry, E.A., Dalby, A.R., Yang, Z.R.: Reduced bio basis function neural network for identification of protein phosphorylation sites: comparison with pattern recognition algorithms. Comput Biol Chem. 28 (2004) 75–85

    Article  MATH  Google Scholar 

  33. Shepherd AJ, Gorse D, T.J.: A novel approach to the recognition of protein architecture from sequence using Fourier analysis and neural networks. Proteins 50 (2003) 290–302

    Article  Google Scholar 

  34. Pollastri, G., Baldi, P., Fariselli, P., Casadio, R.: Improved prediction of the number of residue contacts in proteins by recurrent neural networks. Bioinformatics 17 (2001) 234–242

    Google Scholar 

  35. Lin, K., May, A.C., Taylor, W.R.: Threading using neural nEtwork (TUNE): the measure of protein sequence-structure compatibility. Bioinformatics 18 (2002) 1350–1357

    Article  Google Scholar 

  36. Cai, YD., Liu, X.J., Chou, K.C.: Prediction of protein secondary structure content by artificial neural network. J Comput Chem. 24 (2003) 727–731

    Article  Google Scholar 

  37. Dietmann, S., Frommel, C: Prediction of 3D neighbours of molecular surface patches in proteins by artificial neural networks. Bioinformatics 18 (2002) 167–174

    Article  Google Scholar 

  38. Riis, S., Krogh, A.: Improving Prediction of Protein Secondary Structure using Structured Neural Networks and Multiple Sequence Alignments. Journal of Computational Biology 3 (1996) 163–183

    Google Scholar 

  39. O’Neill, M.C., Song, L.: Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect. BMC Bioinformatics 4 (2003) 13–20

    Article  Google Scholar 

  40. Bicciato, S., Pandin, M., Didone, G, Bello, C.D.: Pattern identification and classification in gene expression data using an autoassociative neural network model. Biotechnol Bioeng. 81 (2003) 594–606

    Article  Google Scholar 

  41. Vohradsky, J.: Neural network model of gene expression. FASEB J. 15 (2001) 846–854

    Article  Google Scholar 

  42. Ando, T., Suguro, M., Hanai, T., Kobayashi, T., Honda, H., Seto, M.: Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma. Jpn J Cancer Res. 93 (2002) 1207–1212

    Google Scholar 

  43. Sawa, T., Ohno-Machado, L.: A neural network-based similarity index for clustering DNA microarray data. Comput Biol Med. 33 (2003) 1–15

    Article  Google Scholar 

  44. Spicker, J.S., Wikman, E, Lu, M.L., Cordon-Cardo, C, Workman, C, ORntoft, T.F., Brunak, S., Knudsen, S.: Neural network predicts sequence of TP53 gene based on DNA chip. Bioinformatics 18 (2002) 1133–1134

    Article  Google Scholar 

  45. Herrero, J., Valencia, A., Dopazo, J.: A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17 (2001) 126–136

    Article  Google Scholar 

  46. Software, P.: PNN Technologies. (Pasadena, CA)

    Google Scholar 

  47. Liang, Y, Georgre, E.O., Kelemen, A.: Bayesian Neural Network for Microarray Data. Technical Report (Department of Mathematical Sciences, University of Memphis, Memphis, TN 38152, U.S.A.)

    Google Scholar 

  48. Ressom, H., Wang, D., Natarajan, P.: Clustering gene expression data using adaptive double self-organizing map. Physiol. Genomics 14 (2003) 35–46

    Google Scholar 

  49. Ritchie, M.D., White, B.C., Parker, J.S., Hahn, L., Moore, J.H.: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4 (2003) 28–36

    Article  Google Scholar 

  50. Dopazo, J., Carazo, J.M.: Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree. J. Mol. Evol. 44 (1997) 226–233

    Article  Google Scholar 

  51. Parbhane, R.V., Tambe, S., Kulkarni, B.D.: Analysis of DNA curvature using artificial neural networks. Bioinformatics 14 (1998) 131–138

    Article  Google Scholar 

  52. Draghici, S., Potter, R.B.: Predicting HIV drug resistance with neural networks. Bioinformatics 19 (2003) 98–107

    Article  Google Scholar 

  53. Alvager, T., Graham, G., Hutchison, D., Westgard, J.: Neural network method to analyze data compression in DNA and RNA sequences. J Chem Inf Comput Sci. 37 (1997) 335–337

    Article  Google Scholar 

  54. Pal, S.K., Polkowski, L., Skowron, A.: Rough-neuro Computing: A way of computing with words. Springer, Berlin (2003)

    Google Scholar 

  55. Pal, S.K., Mitra, S.: Neuro-fuzzy Pattern Recognition: Methods in Soft Computing Paradigm. John Wiley, NY (1999)

    Google Scholar 

  56. Pal, S.K., Shiu, S.C.K.: Foundations of Soft Case Based Reasoning. John Wiley, NY (2004)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, India

    Shubhra Sankar Ray, Sanghamitra Bandyopadhyay, Pabitra Mitra & Sankar K. Pal

Authors
  1. Shubhra Sankar Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanghamitra Bandyopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pabitra Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sankar K. Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ray, S.S., Bandyopadhyay, S., Mitra, P., Pal, S.K. (2005). Neurocomputing for Certain Bioinformatics Tasks. In: Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32370-8_34

Download citation

  • DOI: https://doi.org/10.1007/3-540-32370-8_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23245-2

  • Online ISBN: 978-3-540-32370-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation