Bayesian Networks for Genomic Analysis
نویسندگان
چکیده
Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their application to the analysis of various types of genomic data, from genomic markers to gene expression data. The examples will highlight the potential of this methodology but also the current limitations and we will describe new research directions that hold the promise to make Bayesian networks a fundamental tool for genome data analysis. ∗Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston MA 02118. Email: [email protected] †Software Engineering Department, University of Granada, Daniel Saucedo Aranda, Granada, 18071 Spain. Email: [email protected] ‡Children’s Hospital Informatics Program and Harvard Partners Center for Genetics and Genomics, Harvard Medical School, HMS New Research Building, 77 Pasteur Avenue, Suite 255, Boston, MA 02115. Email: marco [email protected]
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