Abstract
The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.
Highlights
Predictive modeling from gene expression data is an important biomedical research task that involves the search for discriminative biomarkers of disease states in a high-dimensional space.Comprehensible models are necessary in order to extract the predictive biomarkers from learned classifiers
We develop an algorithm to perform the local structure search, the BayesianRule Learning (BRL)-LSS, and evaluate it for parsimony and classification performance using recent gene expression data obtained from public repositories
We choose the BRL1000 version of the algorithm and rename it Bayesian Rule Learning-Global Structure Search (BRL-GSS) to be consistent with nomenclature for the local structure search algorithm that we present
Summary
Predictive modeling from gene expression data is an important biomedical research task that involves the search for discriminative biomarkers of disease states in a high-dimensional space.Comprehensible models are necessary in order to extract the predictive biomarkers from learned classifiers. Predictive modeling from gene expression data is an important biomedical research task that involves the search for discriminative biomarkers of disease states in a high-dimensional space. We have previously demonstrated that rule learning methods can be successfully applied to biomarker discovery from such high-dimensional and low sample-size biomedical data [1,2,3,4,5,6,7,8]. BRL used a global search of the space of constrained Bayesian network structures to infer a set of classification rules containing a posterior probability representing their validity. These rules are readily comprehensible and contain biomarkers and their cut-off values that discriminate among the states of the target/class variable
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