2007/06/30 by Edoardo M. Airoldi, Edoardo M Airoldi · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Bioinformatics and Genomic Networks #Gene expression and cancer classification #Machine Learning in Bioinformatics #cs.LG #physics.soc-ph #q-bio.QM #stat.ME #stat.ML
paper · pdf · doi:10.1371/journal.pcbi.0030252
published as Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252 · 12 pages, 1 figure
arxiv created 2007/11/10 · openalex publication_date 2007/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
robabilistic 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 Message sketches out some answers and illustrates the main ideas behind the statistical approach to biological pattern discovery.