Abstract

Mary E. Edgerton¹, Douglas H. Fisher², Lianhong Tang³, Lewis J. Frey⁴ and Zhihua Chen⁵

¹Department of Pathology and Department of Biomedical Informatics, Vanderbilt University, currently Department of Anatomic Pathology, University of Texas, M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030. ²Department of Electrical Engineering and Computer Science, Vanderbilt University. ³Vanderbilt-Ingram Cancer Center, currently Department of Biomedical Informatics, Vanderbilt University. ⁴Department of Biomedical Informatics, Vanderbilt University, currently Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, ⁵Department of Electrical Engineering and Computer Science, Vanderbilt University, currently Department of Interdisciplinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, SRB3,12902 Magnolia Drive, Tampa, FL 33612.

Type: Original Research

Abstract: We use Backward Chaining Rule Induction (BCRI), a novel data mining method for hypothesizing causative mechanisms, to mine lung cancer gene expression array data for mechanisms that could impact survival. Initially, a supervised learning system is used to generate a prediction model in the form of “IF THEN ” style rules. Next, each antecedent (i.e. an IF condition) of a previously discovered rule becomes the outcome class for subsequent application of supervised rule induction. This step is repeated until a termination condition is satisfi ed. “Chains” of rules are created by working backward from an initial condition (e.g. survival status). Through this iterative process of “backward chaining,” BCRI searches for rules that describe plausible gene interactions for subsequent validation. Thus, BCRI is a semi-supervised approach that constrains the search through the vast space of plausible causal mechanisms by using a top-level outcome to kick-start the process. We demonstrate the general BCRI task sequence, how to implement it, the validation process, and how BCRI-rules discovered from lung cancer microarray data can be combined with prior knowledge to generate hypotheses about functional genomics.

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