Systems modeling and quantitative analysis of large amounts of complex clinical and biological data may help to identify discriminatory patterns that can uncover health risks, detect early disease formation, monitor treatment and prognosis, and predict treatment outcome. In this talk, we describe a machine-learning framework for classification in medicine and biology. It consists of a pattern recognition module, a feature selection module, and a classification modeler and solver. The pattern recognition module involves automatic image analysis, genomic pattern recognition, and spectrum pattern extractions. The feature selection module consists of a combinatorial selection algorithm where discriminatory patterns are extracted from among a large set of pattern attributes. These modules are wrapped around the classification modeler and solver into a machine learning framework. The classification modeler and solver consist of novel optimization-based predictive models that maximize the correct classification while constraining the inter-group misclassifications. The classification/predictive models 1) have the ability to classify any number of distinct groups; 2) allow incorporation of heterogeneous, and continuous/time-dependent types of attributes as input; 3) utilize a high-dimensional data transformation that minimizes noise and errors in biological and clinical data; 4) incorporate a reserved-judgement region that provides a safeguard against over-training; and 5) have successive multi-stage classification capability. Successful applications of our model to developing rules for gene silencing in cancer cells, predicting the immunity of vaccines, identifying the cognitive status of individuals, and predicting metabolite concentrations in humans will be discussed. We acknowledge our clinical/biological collaborators: Dr. Vertino (Winship Cancer Institute, Emory), Drs. Pulendran and Ahmed (Emory Vaccine Center), Dr. Levey (Neurodegenerative Disease and Alzheimer’s Disease), and Dr. Jones (Clinical Biomarkers, Emory).


Support Vector Machine Discriminant Analysis Yellow Fever Vaccine Discriminatory Pattern Machine Learn Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brooks, J.P., Lee, E.K.: Solving a Mixed-Integer Programming Formulation of a Multi-Category Constrained Discrimination Model. In: INFORMS Proceedings of Artificial Intelligence and Data Mining, pp. 1–6 (2006)Google Scholar
  2. 2.
    Brooks, J.P., Lee, E.K.: Analysis of the Consistency of a Mixed Integer Programming-based Multi-Category Constrained Discriminant Model. Annals of Operations Research on Data Mining (Early version appeared online) (in press, 2008)Google Scholar
  3. 3.
    Feltus, F.A., Lee, E.K., Costello, J.F., Plass, C., Vertino, P.M.: Predicting Aberrant CpG Island Methylation. Proceedings of the National Academy of Sciences 100(21), 12253–122558 (2003)CrossRefGoogle Scholar
  4. 4.
    Feltus, F.A., Lee, E.K., Costello, J.F., Plass, C., Vertino, P.M.: DNA Signatures Associated with CpG island Methylation States. Genomics 87, 572–579 (2006)CrossRefGoogle Scholar
  5. 5.
    Gallagher, R.J., Lee, E.K., Patterson, D.: An Optimization Model for Constrained Discriminant Analysis and Numerical Experiments with Iris, Thyroid, and Heart Disease Datasets. In: Cimino, J.J. (ed.) Proceedings of the 1996 American Medical Informatics Association, pp. 209–213 (1996)Google Scholar
  6. 6.
    Gallagher, R.J., Lee, E.K., Patterson, D.A.: Constrained discriminant analysis via 0/1 mixed integer programming. Annals of Operations Research 74, 65–88 (1997) (Special Issue on Non-Traditional Approaches to Statistical Classification and Regression)CrossRefzbMATHGoogle Scholar
  7. 7.
    Lee, E.K., Gallagher, R.J., Patterson, D.: A Linear Programming Approach to Discriminant Analysis with a Reserved Judgment Region. INFORMS Journal on Computing 15(1), 23–41 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Lee, E.K.: Large-scale optimization-based classification models in medicine and biology. Annals of Biomedical Engineering, Systems Biology and Bioinformatics 35(6), 1095–1109 (2007)CrossRefGoogle Scholar
  9. 9.
    Lee, E.K., Easton, T., Gupta, K.: Novel evolutionary models and applications to sequence alignment problems. Annals of Operations Research – Computing and Optimization in Medicine and Life Sciences 148, 167–187 (2006)zbMATHGoogle Scholar
  10. 10.
    Lee, E.K., Fung, A.Y.C., Brooks, J.P., Zaider, M.: Automated Tumor Volume Contouring in Soft-Tissue Sarcoma Adjuvant Brachytherapy Treatment. International Journal of Radiation Oncology, Biology and Physics 47(11), 1891–1910 (2002)Google Scholar
  11. 11.
    Lee, E.K., Gallagher, R., Campbell, A., Prausnitz, M.: Prediction of ultrasound-mediated disruption of cell membranes using machine learning techniques and statistical analysis of acoustic spectra. IEEE Transactions on Biomedical Engineering 51(1), 1–9 (2004)CrossRefGoogle Scholar
  12. 12.
    Lee, E.K., Galis, Z.S.: Fingerprinting Native and Angiogenic Microvascular Networks through Pattern Recognition and Discriminant Analysis of Functional Perfusion Data (submitted, 2008)Google Scholar
  13. 13.
    Lee, E.K., Ashfaq, S., Jones, D.P., Rhodes, S.D., Weintrau, W.S., Hopper, C.H., Vaccarino, V., Harrison, D.G., Quyyumi, A.A.: Prediction of early atherosclerosis in healthy adults via novel markers of oxidative stress and d-ROMs. Working paper (2009)Google Scholar
  14. 14.
    Lee, E.K., Wu, T.L.: Classification and disease prediction via mathematical programming. In: Seref, O., Kundakcioglu, O.E., Pardalos, P. (eds.) Data Mining, Systems Analysis, and Optimization in Biomedicine, AIP Conference Proceedings, vol. 953, pp. 1–42 (2007)Google Scholar
  15. 15.
    McCabe, M., Lee, E.K., Vertino, P.M.: A Multi-Factorial Signature of DNA Sequence and Polycomb Binding Predicts Aberrant CpG Island Methylation. Cancer Research 69(1), 282–291 (2009)CrossRefGoogle Scholar
  16. 16.
    Querec, T.D., Akondy, R., Lee, E.K., et al.: Systems biology approaches predict immunogenicity of the yellow fever vaccine in humans. Nature Immunology 10, 116–125 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Eva K. Lee
    • 1
  1. 1.Center for Operations Research in Medicine and HealthCare, School of Industrial and Systems Engineering, NSF I/UCRC Center for Health Organization Transformation, Center for Bioinformatics and Computational GenomicsGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations