Dataset Information

FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

ABSTRACT: To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.

SUBMITTER: Ernst M

PROVIDER: S-EPMC5311990 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

Ernst Mathias M Du Yang Y Warsow Gregor G Hamed Mohamed M Endlich Nicole N Endlich Karlhans K Murua Escobar Hugo H Sklarz Lisa-Madeleine LM Sender Sina S Junghanß Christian C Möller Steffen S Fuellen Georg G Struckmann Stephan S

Scientific reports 20170216

To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both ...[more]

PMID: 28205611

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Dataset Information

FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

Publications

FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

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