title: A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data.
abstract: In this paper, we present a Bayesian approach to estimate a chromosome and a disorder network from the Online Mendelian Inheritance in Man (OMIM) database. In contrast to other approaches, we obtain statistic rather than deterministic networks enabling a parametric control in the uncertainty of the underlying disorder-disease gene associations contained in the OMIM, on which the networks are based. From a structural investigation of the chromosome network, we identify three chromosome subgroups that reflect architectural differences in chromosome-disorder associations that are predictively exploitable for a functional analysis of diseases.
authors: Computational Biology and Machine Learning Lab, Center forCancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, 97 Lisburn Road, Belfast, UK. v@bio-complexity.com
authors: Frank
authors: F
authors: Emmert-Streib
authors: Ricardo
authors: R
authors: de Matos Simoes
authors: Shailesh
authors: S
authors: Tripathi
authors: Galina V
authors: GV
authors: Glazko
authors: Matthias
authors: M
authors: Dehmer
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D025063:Chromosome Disorders / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D056726:Genetic Association Studies; D006801:Humans; D020407:Internet; D008126:Lod Score; D008297:Male; D015233:Models, Statistical; D058977:Molecular Sequence Annotation; D016366:Open Reading Frames; D020161:Physical Chromosome Mapping