The EM Algorithm and the Rise of Computational Biology

TL;DR

This paper reviews how the EM algorithm has significantly contributed to the growth of computational biology by enabling solutions to key problems in genetics, molecular biology, and data analysis.

Contribution

It provides a comprehensive survey of the EM algorithm's application across various important computational biology problems, highlighting its pivotal role.

Findings

EM algorithm facilitated advances in sequence motif discovery

Enabled improvements in protein sequence alignment

Supported developments in population genetics and gene expression analysis

Abstract

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.