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...
This is a preview of subscription content, log in via an institution.
Recommended Reading
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.
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.
Bonneau, R., & Baker, D. (2001). Ab initio protein structure prediction: Progress and prospects. Annual Review of Biophysics and Biomolecular Structure, 30, 173–189.
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.
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.
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).
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).
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.
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.
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.
Friedman, N. (2000). Being Bayesian about network structure. In Machine Learning, 50, 95–125.
Friedman, N., & Halpern, J. (1999). Modeling beliefs in dynamic systems. part ii: Revision and update. Journal of AI Research, 10, 117–167.
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.
Getoor, L., & Taskar, B. (2007). Introduction to statistical relational learning. Cambridge, MA: MIT Press.
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.
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).
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.
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.
KDD cup (2001). http://pages.cs.wisc.edu/~dpage/kddcup2001/.
Klösgen, W. (2002). Handbook of data mining and knowledge discovery, chapter 16.3: Subgroup discovery. New York: Oxford University Press.
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.
Mardis, E. R. (2006). Anticipating the 1,000 dollar genome. Genome Biology, 7(7), 112.
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.
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.
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.
Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20), 629–679.
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.
Oliver, S. G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., et al. (2009). The automation of science. Science, 324, 85–89.
Ong, I., Glassner, J., & Page, D. (2002). Modelling regulatory pathways in e.coli from time series expression profiles. Bioinformatics, 18, 241S–248S.
Pe’er, D., Regev, A., Elidan, G., & Friedman, N. (2001). Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17, 215–224.
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.
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.
Rost, B., & Sander, C. (1993). Prediction of protein secondary structure at better than 70 accuracy. Journal of Molecular Biology, 232, 584–599.
Segal, E., Pe’er, D., Regev, A., Koller, D., & Friedman, N. (April 2005). Learning module networks. Journal of Machine Learning Research, 6, 557–588.
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.
Srinivasan, A. (2001). The aleph manual. http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/.
Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genome-wide studies. Proceedings of the National Academy of Sciences, 100, 9440–9445.
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.
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.
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.
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.
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.
Zhang, X., Mesirov, J. P., & Waltz, D. L. (1992). Hybrid system for protein secondary structure prediction. Journal of Molecular Biology, 225, 81–92.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
Page, C.D., Natarajan, S. (2011). Biomedical Informatics. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_81
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_81
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering