Application of Machine-Learning Methods to Understand Gene Expression Regulation

• Published in: Genetic Programming Theory and Practice XII pp. 1 - 15
• Main Authors: Cheng, Chao, Worzel, William P.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 05.06.2015
• Series: Genetic and Evolutionary Computation
• Subjects: ChIP-Chip; ChIP-seq; ENCODE; Genetic Programming (GP); Histone modification; modENCODE; Random forest; RNA-seq; Support Vector Machine (SVM); Transcription Factor (TF)
• ISBN: 331916029X; 9783319160290
• ISSN: 1932-0167
• DOI: 10.1007/978-3-319-16030-6_1

With the development and application of high-throughput technologies, an enormous amount of biological data has been produced in the past few years. These large-scale datasets make it possible and necessary to implement machine learning techniques for mining biological insights. In this chapter, we describe several examples to show how machine learning approaches are used to elucidate the mechanism of transcriptional regulation mediated by transcription factors and histone modifications. We demonstrate that machine learning provides powerful tools to quantitatively relate gene expression with transcription factor binding and histone modifications, to identify novel regulatory DNA elements in the genomes, and to predict gene functions. We also discuss the advantages and limitations of genetic programming in analyzing and processing biological data.