Discovering causal interactions using Bayesian network scoring and information gain

BMC Bioinformatics

Methodology Article

Xia Jiang¹  Zexian Zeng²  Richard Neapolitan² 

[1] Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA;Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA;

关键词: Bayesian network;    Cause;    Interaction;    Information gain;    Epistasis;    Low-dimensional;    Breast cancer survival;   

DOI  :  10.1186/s12859-016-1084-8

 received in 2015-11-27, accepted in 2016-05-14,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundThe problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data. Motivated by the problem of learning epistatic interactions from datasets developed in genome-wide association studies (GWAS), researchers conceived new methods for learning discrete interactions. However, many of these methods do not differentiate a model representing a true interaction from a model representing non-interacting causes with strong individual affects. The recent algorithm MBS-IGain addresses this difficulty by using Bayesian network learning and information gain to discover interactions from high-dimensional datasets. However, MBS-IGain requires marginal effects to detect interactions containing more than two causes. If the dataset is not high-dimensional, we can avoid this shortcoming by doing an exhaustive search.ResultsWe develop Exhaustive-IGain, which is like MBS-IGain but does an exhaustive search. We compare the performance of Exhaustive-IGain to MBS-IGain using low-dimensional simulated datasets based on interactions with marginal effects and ones based on interactions without marginal effects. Their performance is similar on the datasets based on marginal effects. However, Exhaustive-IGain compellingly outperforms MBS-IGain on the datasets based on 3 and 4-cause interactions without marginal effects. We apply Exhaustive-IGain to investigate how clinical variables interact to affect breast cancer survival, and obtain results that agree with judgements of a breast cancer oncologist.ConclusionsWe conclude that the combined use of information gain and Bayesian network scoring enables us to discover higher order interactions with no marginal effects if we perform an exhaustive search. We further conclude that Exhaustive-IGain can be effective when applied to real data.

【 授权许可】

CC BY   
© Zeng et al. 2016