1 Data and Text Mining Bayesian Rule Learning for Biomedical Data Mining

Motivation: Disease state prediction from biomarker profiling stud-ies is an important problem because more accurate classification models will potentially lead to the discovery of better, more discrimi-native markers. Data mining methods are routinely applied to such analyses of biomedical datasets generated from high-throughput ‘omic ’ technologies applied to clinical samples from tissues or bodily fluids. Past work has demonstrated that rule models can be suc-cessfully applied to this problem, While many rule-based methods produce rules that make predictions under uncertainty, they typically do not quantify the uncertainty in the validity of the rule itself. This paper describes an approach that uses a Bayesian score to evalu-ate rule models. Results: We have combined the expressiveness of rules with the mathematical rigor of Bayesian networks to develop and evaluate a Bayesian Rule Learning (BRL) system. This system utilizes a novel variant of the K2 algorithm for building Bayesian networks from the training data to provide probabilistic scores for IF-antecedent-THEN-consequent rules using heuristic best-first search. We then apply rule-based inference to evaluate the learned models during 10-fold cross-validation performed two times. The BRL system is evaluated on 24 published ‘omic ’ datasets, and on average it performs on par or better than other readily available rule learning methods. More-over, BRL produces models that contain on average 70 % fewer variables, which means that the biomarker panels for disease pre-diction contain fewer markers for further verification and validation by bench scientists