Application of Machine-Learning Methods to Understand Gene Expression Regulation

Abstract

With the development and application ofhigh-throughput technologies, an enormous amount ofbiological data has been produced in the past fewyears. These large-scale datasets make it possible andnecessary to implement machine learning techniques formining biological insights. In this chapter, wedescribe several examples to show how machine learningapproaches are used to elucidate the mechanism oftranscriptional regulation mediated by transcriptionfactors and histone modifications. We demonstrate thatmachine learning provides powerful tools toquantitatively relate gene expression withtranscription factor binding and histone modifications,to identify novel regulatory DNA elements in thegenomes, and to predict gene functions. We also discussthe advantages and limitations of genetic programmingin analysing and processing biological data.