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Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure

Lustgarten, Jonathan and Balasubramanian, Jeya and Visweswaran, Shyam and Gopalakrishnan, Vanathi (2017) Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure. Data, 2 (1). ISSN 2306-5729

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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 in the number of predictor variables in the model. We relax these global constraints to learn a more expressive local structure with BRL-LSS. BRL-LSS entails a 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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Lustgarten, Jonathan
Visweswaran, Shyamshv3@pitt.edushv3
Gopalakrishnan, Vanathivanathi@pitt.eduvanathi
Date: 18 January 2017
Date Type: Publication
Journal or Publication Title: Data
Volume: 2
Number: 1
Publisher: MDPI AG
DOI or Unique Handle: 10.3390/data2010005
Schools and Programs: School of Computing and Information > Intelligent Systems Program
Refereed: Yes
Uncontrolled Keywords: rule based models; gene expression data; Bayesian networks; parsimony
ISSN: 2306-5729
Official URL:
Funders: National Library of Medicine of the National Institutes of Health, National Institute of General Medical Sciences of the National Institutes of Health
Article Type: Research Article
Date Deposited: 04 Feb 2021 18:11
Last Modified: 04 Feb 2021 18:11


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