Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Biomedical Informatics

  • C. David Page
  • Sriraam Natarajan
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_81

Introduction

Recent years have witnessed a tremendous increase in the use of machine learning for biomedical applications. This surge in interest has several causes. One is the successful application of machine learning technologies in other fields such as web search, speech and handwriting recognition, agent design, spatial modeling, etc. Another is the development of technologies that enable the production of large amounts of data in the time it used to take to generate a single data point (run a single experiment). A third most recent development is the advent of Electronic Medical/Health Records (EMRs/EHRs). The drastic increase in the amount of data generated has led the biologists and clinical researchers to adopt algorithms that can construct predictive models from large amounts of data. Naturally, machine learning is emerging as a tool of choice.

In this article, we will present a few data types and tasks involving such large-scale biological data, where machine learning...

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  1. Ananiev, G. E., Goldstein, S., Runnheim, R., Forrest, D. K., Zhou, S., Potamousis, K., Churas, C. P., Bergendah, V., Thomson, J. A., & David, C. (2008). Schwartz1. Optical mapping discerns genome wide DNA methylation profiles. BMC Molecular Biology, 9, doi:10.1186/1471-2199-9-68.Google Scholar
  2. Baggerly, K., Morris, J. S., & Combes, K. R. (2004). Reproducibility of seldi-tof protein patterns in serum: Comparing datasets from different experiments. Bioinformatics, 20, 777–785.Google Scholar
  3. Bonneau, R., & Baker, D. (2001). Ab initio protein structure prediction: Progress and prospects. Annual Review of Biophysics and Biomolecular Structure, 30, 173–189.Google Scholar
  4. Burnside, E. S., Davis, J., Chhatwal, J., Alagoz, O., Lindstrom, M. J., Geller, B. M., Littenberg, B., Kahn, C. E., Shaffer, K., & Page, D. (2009). Unique features of hla-mediated hiv evolution in a mexican cohort: A comparative study. Radiology, 251, 663–672.Google Scholar
  5. Carlson, J., Valenzuela-Ponce, H., Blanco-Heredia, J., Garrido-Rodriguez, D., Garcia-Morales, C., Heckerman, D., et al. (2009). Unique features of hla-mediated hiv evolution in a mexican cohort: A comparative study. Retrovirology, 6(72), 39.Google Scholar
  6. Davis, J., Costa, V. S., Ray, S., & Page, D. (2007a). An integrated approach to feature construction and model building for drug activity prediction. In Proceedings of the 24th international conference on machine learning (ICML).Google Scholar
  7. Davis, J., Ong, I., Struyf, J., Burnside, E., Page, D., & Costa, V. S. (2007b). Change of representation for statistical relational learning. In Proceedings of the 20th international joint conference on artificial intelligence (IJCAI).Google Scholar
  8. DiMaio, F., Kondrashov, D., Bitto, E., Soni, A., Bingman, C., Phillips, G., & Shavlik, J. (2007). Creating protein models from electron-density maps using particle-filtering methods. Bioinformatics, 23, 2851–2858.Google Scholar
  9. Easton, D. F., Pooley, K. A., Dunning, A. M., Pharoah, P. D., et al. (2007). Genome-wide association study identifies novel breast cancer susceptibility loci. Nature, 447, 1087–1093.Google Scholar
  10. Finn, P., Muggleton, S., Page, D., & Srinivasan, A. (1998). Discovery of pharmacophores using the inductive logic programming system progol. Machine Learning, 30(1, 2), 241–270.Google Scholar
  11. Friedman, N. (2000). Being Bayesian about network structure. In Machine Learning, 50, 95–125.Google Scholar
  12. Friedman, N., & Halpern, J. (1999). Modeling beliefs in dynamic systems. part ii: Revision and update. Journal of AI Research, 10, 117–167.zbMATHMathSciNetGoogle Scholar
  13. Furey, T. S., Cristianini, N., Duffy, N., Bednarski, B. W., Schummer, M., & Haussler, D. (2000). Support vector classification and validation of cancer tissue samples using microarray expression. Bioinformatics, 16(10), 906–914.Google Scholar
  14. Getoor, L., & Taskar, B. (2007). Introduction to statistical relational learning. Cambridge, MA: MIT Press.zbMATHGoogle Scholar
  15. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531–537.Google Scholar
  16. Hardin, J., Waddell, M., Page, C. D., Zhan, F., Barlogie, B., Shaughnessy, J., et al. (2004). Evaluation of multiple models to distinguish closely related forms of disease using DNA microarray data: An application to multiple myeloma. Statistical Applications in Genetics and Molecular Biology, 3(1).Google Scholar
  17. Jain, A. N., Dietterich, T. G., Lathrop, R. H., Chapman, D., Critchlow, R. E., Bauer, B. E., et al. (1994). Compass: A shape-based machine learning tool for drug design. Aided Molecular Design, 8(6), 635–652.Google Scholar
  18. Jones, K. E., Reiser, F. M., Bryant, P. G. K., Muggleton, C. H., Kell, S., King, D. B., et al. (2004). Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427, 247–252.Google Scholar
  19. Klösgen, W. (2002). Handbook of data mining and knowledge discovery, chapter 16.3: Subgroup discovery. New York: Oxford University Press.Google Scholar
  20. Listgarten, J., Damaraju, S., Poulin, B., Cook, L., Dufour, J., Driga, A., et al. (2004). Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms. Clinical Cancer Research, 10, 2725–2737.Google Scholar
  21. Mardis, E. R. (2006). Anticipating the 1,000 dollar genome. Genome Biology, 7(7), 112.Google Scholar
  22. Martin, Y. C., Bures, M. G., Danaher, E. A., DeLazzer, J., Lico, I. I., & Pavlik, P. A. (1993). A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. Journal of Computer Aided Molecular Design, 8, 751–758.Google Scholar
  23. McCarty, C., Wilke, R. A., Giampietro, P. F, Wesbrook, S. D., & Caldwell, M. D. (2005). Personalized Medicine Research Project (PMRP): Design, methods and recruitment for a large population-based biobank. Personalized Medicine, 2, 49–79.Google Scholar
  24. Molla, M., Waddell, M., Page, D., & Shavlik, J. (2004). Using machine learning to design and interpret gene expression microarrays. AI Magazine, 25(1), 23–44.Google Scholar
  25. Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20), 629–679.MathSciNetGoogle Scholar
  26. Noto, K., & Craven, M. (2006). A specialized learner for inferring structured cis-regulatory modules. BMC Bioinformatics, 7(528), doi:10.1186/1471-2105-7-528.Google Scholar
  27. Oliver, S. G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., et al. (2009). The automation of science. Science, 324, 85–89.Google Scholar
  28. Ong, I., Glassner, J., & Page, D. (2002). Modelling regulatory pathways in e.coli from time series expression profiles. Bioinformatics, 18, 241S–248S.Google Scholar
  29. Pe’er, D., Regev, A., Elidan, G., & Friedman, N. (2001). Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17, 215–224.Google Scholar
  30. Perou, C., Jeffrey, S., Van De Rijn, M., Rees, C. A., Eisen, M. B., Ross, D. T., et al. (1999). Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proccedings of National Academy of Science, 96, 9212–9217.Google Scholar
  31. Petricoin, E. F., III, Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., et al. (2002). Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359, 572–577.Google Scholar
  32. Rost, B., & Sander, C. (1993). Prediction of protein secondary structure at better than 70 accuracy. Journal of Molecular Biology, 232, 584–599.Google Scholar
  33. Segal, E., Pe’er, D., Regev, A., Koller, D., & Friedman, N. (April 2005). Learning module networks. Journal of Machine Learning Research, 6, 557–588.MathSciNetGoogle Scholar
  34. Spatola, A., Page, D., Vogel, D., Blondell, S., & Crozet, Y. (1999). Can machine learning and combinatorial chemistry co-exist? In Proceedings of the American Peptide Symposium. Kluwer Academic Publishers.Google Scholar
  35. Srinivasan, A. (2001). The aleph manual. http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/.
  36. Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genome-wide studies. Proceedings of the National Academy of Sciences, 100, 9440–9445.zbMATHMathSciNetGoogle Scholar
  37. The International Warfarin Pharmacogenetics Consortium (IWPC) (2009). Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data. The New England Journal of Medicine, 360:753–764.Google Scholar
  38. Tucker, A., Vinciotti, V., Hoen, P. A. C., Liu, X., & Famili, A. F. (2005). Bayesian network classifiers for time-series microarray data. Advances in Intelligent Data Analysis VI, 3646, 475–485.Google Scholar
  39. Van’t Veer, L. L., Dai, H., van de Vijver, M. M., He, Y., Hart, A., Mao, M., et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530–536.Google Scholar
  40. Waddell, M., Page, D., & Shaughnessy, J., Jr. (2005). Predicting cancer susceptibility from single-nucleotide polymorphism data: A case study in multiple myeloma. BIOKDD’05: Proceedings of the fifth international workshop on bioinformatics, Chicago, IL.Google Scholar
  41. Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. In European symposium on principles of kdd (pp. 78–87). Lecture notes in computer science, Springer, Norway.Google Scholar
  42. Zhang, X., Mesirov, J. P., & Waltz, D. L. (1992). Hybrid system for protein secondary structure prediction. Journal of Molecular Biology, 225, 81–92.Google Scholar
  43. Zou, M., & Conzen, S. D. (2005). A new dynamic Bayesian network approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21, 71–79.Google Scholar

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • C. David Page
  • Sriraam Natarajan

There are no affiliations available