Abstract
Bioinformatics brings together several disciplines — molecular biology, genetics, microbiology, mathematics, chemistry and bio-chemistry, physics, and of course informatics. Many processes in biology, as discussed in Chapter 1, are dynamically evolving and their modelling requires evolving methods and systems. In bioinformatics new data is being made available with a tremendous speed that would require the models to be continuously adaptive. Knowledge-based modelling that includes rule and knowledge discovery is a crucial requirement. All these issues make the evolving connectionist methods and systems needed for problem solving across areas of bioinformatics, from DNA sequence analysis, through gene expression data analysis, through protein analysis, and finally to modelling genetic networks and entire cells. That will help to discover genetic profiles and to better understand diseases that do not yet have a cure, and to better understand what the human body is made of and how it works in its complexity at its different levels of organisation (see Fig. 1.1).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Further Reading
Futschik, M. (2002) Intelligent systems for knowledge discovery in molecular biology. PhD Thesis, University of Otago, New Zealand.
Futschik, M. and Kasabov, N. (2002) Fuzzy clustering in gene expression data analysis. Proc. World Congress of Computational Intelligence WCCI’2002, Hawaii. IEEE Press.
Pevzner, P. (2001) Computational Molecular Biology. MIT Press, Cambridge, MA.
Attwood, T. and Parry-Smith, D. (1999) Introduction to Bioinformatics. Cell and Molecular Biology in Action. Addison Wesley Longman, Harlow.
Baldi, P. and Brunak, S. (1998, 2001) Bioinformatics — a Machine Learning Approach. MIT Press, Cambridge, MA.
Boguski, M. (1998) Bioinformatics–a new era. Trends Guide to Bioinformatics (Supplement to Immunology Today, 19 (11)), pp. 1–3.
Brown, C., Shreiber, M., Chapman, B. and Jacobs, G. (2000) Information science and bioinformatics, In Future Directions of Intelligent Systems and Information Sciences (ed. N. Kasabov ). Physica-Verlag (Springer-Verlag), Heidelberg, pp. 251–287.
Pevzner, P. (2001) Computational Molecular Biology. MIT Press, Cambridge, MA.
Hofstadter, D. (1979) Gödel, Escher, Bach: an Eternal Golden Braid. Basic Books, New York.
Wu, C. H. and McLarty, J. W. (2000) Neural Networks and Genome Informatics. Elsevier, Amsterdam.
Mount, S. (1982) A catalogue of splice junction sequences. Nucleic Acids Research, 10 (2), 459–472.
Schena, M. (ed.) (2000) Microarray Biochip Technology, Eaton Publishing, Natick, MA.
Persidis, A. (2000) Data mining in biotechnology. Nature, 18 (2), 237–238.
Collado-Vides, J., Magasanik, B. and Smith, T. F. (1996) Integrative Approaches to Molecular Biology. MIT Press, Cambridge, MA.
Bower, J. M. and Bolouri, H. (eds.) (2001) Computational Modelling of Genetic and Biochemical Networks. MIT Press, Cambridge, MA.
D’haeseleer, P., Liang, S. and Somogyi, R. (2000) Genetic network inference; from co-expression clustering to reverse engineering. Bioinformatics, 16 (8), 707–726.
Snow, C. and Ferguson, C. (1977) Talking to Children: Language Input and Language Acquisition. Cambridge University Press, Cambridge.
Gibson, M. A. and Mjolsness, E. (2001) Modelling the activity of single genes. In Computational Modelling of Genetic and Biochemical Networks (eds. J. M. Bower and H. Bolouri ). MIT Press, Cambridge, MA. pp. 3–48.
Futschik, M. (2002) Intelligent systems for knowledge discovery in molecular biology. PhD Thesis, University of Otago, New Zealand.
Futschik, M. (2002) Intelligent systems for knowledge discovery in molecular biology. PhD Thesis, University of Otago, New Zealand.
Futschik, M. and Kasabov, N. (2001) Evolving fuzzy neural networks for knowledge discovery from gene expression data - a case study. RECOMB’2001 Proceedings - Currents in Computational Molecular Biology 2001 (eds. T. Lengauer and D. Sankoff). Montreal, 22–25 April, pp. 175–178.
Futschik, M. and Kasabov, N. (2002) Fuzzy clustering in gene expression data analysis. Proc. World Congress of Computational Intelligence WCCI’2002, Hawaii. IEEE Press.
Schneider, G. and Wrede, P. (1993) Development of artificial neural filters for pattern recognition in protein sequences. Journal of Molecular Evolution, 36, 586–595.
Schaff, J. and Loew, L. (1999) The virtual cell. Pacific Symposium of Biocomputing, 4, 228–239.
Tomita, M. 1999 Whole-cell simulation: a grand challenge of the 21st century. Trends in Biotechnology, 19 (6), 205–210.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag London
About this chapter
Cite this chapter
Kasabov, N. (2003). Data Analysis, Modelling and Knowledge Discovery in Bioinformatics. In: Evolving Connectionist Systems. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3740-5_8
Download citation
DOI: https://doi.org/10.1007/978-1-4471-3740-5_8
Publisher Name: Springer, London
Print ISBN: 978-1-85233-400-0
Online ISBN: 978-1-4471-3740-5
eBook Packages: Springer Book Archive