Belief Networks for Bioinformatics

Recent publications illustrate successful applications of belief networks1 (BNs) and related probabilistic networks in the domain of bioinformatics. Examples are the modeling of gene regulation networks [6,14,26], the discovering of metabolic [40,83] and signalling pathways [94], sequence analysis [9, 10], protein structure [16, 28, 76], and linkage analysis [55]. Belief networks are applied broadly in health care and medicine for diagnosis and as a data mining tool [57, 60, 61]. New developments in learning belief networks from heterogeneous data sources [40, 56, 67, 80, 82, 96] show that belief networks are becoming an important tool for dealing with high-throughput data at a large scale, not only at the genetic and biochemical level, but also at the level of systems biology.

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