Getting started in probabilistic graphical models

Abstract

Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses that are testable at the bench? This note sketches out some answers and illustrates the main ideas behind the statistical approach to biological pattern discovery.

12 pages, 1 figure

References in corpus (2)

• A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data
• Mixed membership analysis of high-throughput interaction studies: Relational data

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