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Data Mining for Bioinformatics— Systems Biology

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Bioinformatics: Applications in Life and Environmental Sciences

Abstract

Biology, like many other sciences, changes when technology brings in new tools that extend the scope of inquiry. The invention of the optical microscope in late 1600 brought an entirely new vista to biology when cellular structures could be more clearly seen by scientists. Much more modern and recent electron microscope developed in the 60’s enhanced the visualization of cells considerably. The application of computing to biological problems has created yet another new opportunity for the biologists of the 21st century. As computers continue to change the society at large, there is not doubt that several years of development in databases, software for data analysis, computational algorithms, computer generated visualization, use of computers to determine structures of complex bio-molecules, computational simulation of ecosystem, analysis of evolutionary pathways and many more computational methods have brought several new dimensions to biology. The technological revolution, from an ordinary computer to high-performance/grid computing, the processes have further automated, and led to flooding of data which cannot be handled properly, due to lack of proper standards at proper time. Billions of records are being pushed by the researchers and scientists into the data repositories across the world.

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References Articles/Papers/Presentations/Books

  • Aravind, L. (2000). Guilt by association: Contextual information in genome analysis, Genome Research, 10: 1074–1077.

    CAS  Google Scholar 

  • Baldi, P. and Brunak, S. (2003). Bioinformatics: The Machine Learning Approach, 2ed. Affiliated East-West Press Pvt. Ltd., New Delhi.

    Google Scholar 

  • D’haeseler, P., Liang, S. and Somogyi, R. (2000). Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics, 16: 707–726.

    Article  Google Scholar 

  • Diehn, M. and Relman, D. (2001). Comparing functional genomic datasets: Lessons from DNA microarray analyses of host-pathogen interactions. Current Opinion on Microbiology, 4: 95–101.

    Article  CAS  Google Scholar 

  • Eisenberg, D., Marcotte, E.M., Xenarios, I. and Yeates, T.O. (2000). Protein function in the post-genomic era. Nature, 405: 823–826.

    Article  PubMed  CAS  Google Scholar 

  • Forst, C.V. and Schulten, K. (1999). Evolution of metabolism: A new method for the comparison of metabolic pathways using genomic information. Journal of Computational Biology, 6: 343–360.

    Article  PubMed  CAS  Google Scholar 

  • Forst, C.V. (2001). A Tutorial on Network Genomics. International Conference on Intelligent Systems for Molecular Biology, Copenhagen, Denmark.

    Google Scholar 

  • Funahashi, A. (2003). Introduction to SBML and SBML compliant software. ERATO Kitano Symbiotic Systems Project, presentation slides. Available at http://sbml.org/ workshops/tokyotutorial/tutorial.htm.

    Google Scholar 

  • Fuente, A. de la and Mendes, P. (2003). Integrative modeling of gene expression and cell metabolism. Applied Bioinformatics, Open Mind Journals, 2(2): 79–90.

    Google Scholar 

  • Goldsby, R.A., Kindt, T.J. and Osborne, B.A. (1999). Kuby Immunology. W. H. Freeman & Co., 4 edition.

    Google Scholar 

  • Han, J. and Kamber, M. (2001). Data Mining: Concepts and Techniques. Elsevier, San Francisco.

    Google Scholar 

  • Hayes, B. (2000). Graph Theory in Practice: Part 1. American Scientist, 88(1). Available at http://www.americanscientist.org/

    Google Scholar 

  • Jagadish, H.V. and Olken, F. (2003). Data Management for the Biosciences. Report of the NSF/NLM Workshop of Data Management for Molecular and Cell Biology, Feb 2003. Available at http://ww.eecs.umich.edu/~jag/wdmbio/ wdmb_rpt.pdf.

    Google Scholar 

  • Jagota, Arun (2001). Microarray data analysis and visualization. Dept. of Computer Engineering, University of California, CA., USA.

    Google Scholar 

  • McAdams, H. and Shapiro, L. (1995). Circuit simulation of genetic networks. Science, 269: 650–656.

    Article  PubMed  CAS  Google Scholar 

  • Mitchell, T.M. (1997). Machine Learning. McGraw Hill International Edition, New Delhi.

    Google Scholar 

  • Schmidt, H. and Jirstrand, M. (2005). Systems Biology Toolbox for MATLAB: A computational platform for research in Systems Biology. Bioinformatics Advance Access, available at http://www.fcc.chalmers.se/~henning/SBTOOLBOX/

    Google Scholar 

  • Somogyi, R. and Sniegoski, C. (1996). Modeling the complexity of genetic networks. Complexity, 1: 45–63.

    Google Scholar 

  • Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. and Church, G.M. (1999). Systematic determination of genetic network architecture. Nature Genetics, 22: 281–285.

    Article  PubMed  CAS  Google Scholar 

  • Ullah, M. and Wolkenhauer, O. (2007). Family tree of Markov models in systems biology. IET Systems Biology, 1(4).

    Google Scholar 

  • White, K.P., Rifkin, S.A., Hurban, P. and Hogness, D.S. (1999). Microarray analysis of Drosophila development during metamorphosis. Science, 286(5447): 2179–2184.

    Article  PubMed  CAS  Google Scholar 

  • Wiechert, W. (2002). Modeling and simulation: Tools for metabolic engineering. Journal of Biotechnology, 94: 37–63.

    Article  PubMed  CAS  Google Scholar 

  • Wolkenhauer, O. (2002). Mathematical modelling in the post-genome era: understanding genome expression and regulation — a system theoretic approach. BioSystems, 65: 1–18.

    Article  PubMed  CAS  Google Scholar 

  • Wooley, J.C. and Lin, H.S. (2001). Catalyzing inquiry at the interface of Computing and Biology. The National Academies Press, Washington D.C. Available at http:/ /genomics.energy.gov.

    Google Scholar 

  • Zhu, H. and Snyder, M. (2001). Protein arrays and microarrays. Current Opinion in Chemical Biology, 5: 40–45.

    Article  PubMed  CAS  Google Scholar 

Websites

  • Biology WorkBench. San Diego Supercomputer Centre, Bioinformatics and Computational Biology group, Department of Bioengineering at University of California, San Diego, Available at http://workbench.sdsc.edu.

    Google Scholar 

  • BioPathways Consortium. BioPathways website. Available at http:// www.biopathways.org.

    Google Scholar 

  • Colantouni, C., Henry, G. and Pevsner, J. (2000). Standardization and Normalization of Microarray Data (SNOMAD) software. Available at http:// pevsnerlab.kennedykrieger.org/snomad.htm.

    Google Scholar 

  • de Hoon, M., Imoto, S. and Miyano, S. (2004). The C Clustering Library (Cluster 3.0) software. University of Tokyo, Institute of Medical Science, Human Genome Center, Japan. Available at http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster.

    Google Scholar 

  • DeRisi Lab. Department of Biochemistry & Biophysics, University of California at San Francisco. Website available at http://www.microarrays.org.

    Google Scholar 

  • European Bioinformatics Institute (EBI). EBI website. Available at http:// www.ebi.ac.uk/biomodels.

    Google Scholar 

  • Gene Expression Data Analysis (GEDA) Tool. GEDA software. University of Pennsylvania MC Health Systems. Available at http://bioinformatics.upmc.edu/GE2/ GEDA.html.

    Google Scholar 

  • Genomes: GTL (32007). Genomes: GTL (formerly Genomes to Life Initiative), US Department of Energy. Available at http://doegenomestolife.org.

    Google Scholar 

  • Harvard Medical School. Department of Systems Biology, Harvard Medical School. Available at http://sysbio.med.harvard.edu/.

    Google Scholar 

  • IBM. IBM Research website. Available at http://www.research.ibm.com/grape/.

    Google Scholar 

  • Institute for Systems Biology (ISB) (2007). ISB website. Available at http:// www.systemsbiology.org.

    Google Scholar 

  • Joint Center for Structural Genomics. Joint Center for Structural Genomics. Available at http://www.jcsg.org.

    Google Scholar 

  • Keck Graduate Institute. Keck Computational Systems Biology (California) website. Available at http://sbw.kgi.edu/.

    Google Scholar 

  • Massachusetts Institute of Technology. Computational and Systems Biology Initiative (MIT) website. Available at http://csbi.mit.edu.

    Google Scholar 

  • Molecular Sciences Institute. Molecular Sciences Institute (Berkeley). Available at http:/www.molsci.org/Dispatch.

    Google Scholar 

  • National Cancer Institute (2002). Gene Expression Data Portal (GEDP), National Institutes of Health, USA. Available at http://gedp.nci.nih/gov/de/servlet/manager.

    Google Scholar 

  • National Centre for Biotechnology Information (2002). Gene Expression Omnibus (GEO), National Institutes of Health, USA. Gene expression datasets available at ftp://ftp.ncbi.nih.gov/pub/geo/data/gds/soft.

    Google Scholar 

  • Ottawa Institute of Systems Biology. Ottawa Institute of Systems Biology (Canada) website. Available at http://mededu.med.uottawa.ca/oisb/eng/.

    Google Scholar 

  • Pacific Northwest National Laboratory. Systems Biology at PNNL. Available at http://www.sysbio.org/.

    Google Scholar 

  • Princeton University. Lewis Siegler Institute for Integrative Genomics, Princeton University. Available at http://www.genomics.princeton.edu/.

    Google Scholar 

  • Princeton University. Center for Systems Biology, Institute for Advanced Study, Princeton University. Available at http://www.csb.ias.edu/.

    Google Scholar 

  • SBML. Caltech ERATO Kiranto Systems Biology Project Group. Available at http:/ /www.cds.caltech.edu/erato.

    Google Scholar 

  • SilicoCyte (2004). SilicoCyte v 1.3 software. Available at http://www.silicocyte.com.

    Google Scholar 

  • Systems Biology Institute, Japan (2003). The Systems Biology Institute (Japan) website. Available at http://www.sbi.jp/indexE.html, see also http://www.systemsbiology.org/.

    Google Scholar 

  • Tom Sawyer (2003). Tom Sawyer software Image Gallery. Website available at http:/ /www.tomsawyer.com/gallery/gallery.php?printable=1.

    Google Scholar 

  • University of Auckland. University of Auckland, New Zealand. Available at http:// www.cellml.org.

    Google Scholar 

  • University of Stuttgart. Systems Biology Group at the University of Stuttgart (Germany) website. Available at http://www.sysbio.de/.

    Google Scholar 

  • Virginia Tech. GEPASI software website, Virginia Bioinformatics Institute, Virginia Tech. Available at http://www.gepasi.org.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Science & Engineering, Lingaya’s Institute of Management & Technology, Faridabad, 121 002, Haryana

    T.V. Prasad (Professor & Head)

  2. Department of Computer Science, Jamia Millia Islamia, New Delhi

    S.I. Ahson (Professor & Head)

Authors
  1. T.V. Prasad
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  2. S.I. Ahson
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Editor information

Editors and Affiliations

  1. Department of Life Sciences, University of Mumbai, India

    M. H. Fulekar

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© 2009 Capital Publishing Company

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Prasad, T., Ahson, S. (2009). Data Mining for Bioinformatics— Systems Biology. In: Fulekar, M.H. (eds) Bioinformatics: Applications in Life and Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8880-3_9

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