Statistical Relational Learning for Genomics Applications: A State-of-the-Art Review

Abstract

AbstractThis paper aims to review the state-of-the-art statistical relational learning models (SRL) in genomics. SRL deals with machine learning and data mining in relational domains where observations may be missing, partially observed, and noisy. This chapter introduces a background overview of various models, including probabilistic graphical models, Bayesian networks, dependency networks, Markov networks, first-order logic, and probabilistic inductive logic programming. This chapter also discusses the various statistical relational learning approaches, including probabilistic relational models, stochastic logic programs, Bayesian logic programs, relational dependency networks, relational Markov networks, and Markov logic networks. Finally, the last part of the paper focuses on the practical application of statistical relational learning techniques in genomics. The chapter concludes with a discussion on the limitations of current methods.KeywordsGenomicArtificial intelligenceMachine learningProbabilisticBayesianMarkovDependencyGeneticsBioinformatics