ArticleScientific reports2017
FocusHeuristics - expression-data-driven network optimization and disease gene prediction.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Network analysis for estimating standardization trends in genomics using MEDLINE.BMC medical research methodology · 2022Article
- Benchmarking network-based gene prioritization methods for cerebral small vessel disease.Briefings in bioinformatics · 2021Article
- Scoring functions for drug-effect similarity.Briefings in bioinformatics · 2021Review
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- Suppression of the TGF-β pathway by a macrolide antibiotic decreases fibrotic responses by ocular fibroblastsRoyal Society open science · 2020Article
- TopControl: A Tool to Prioritize Candidate Disease-associated Genes based on Topological Network Features.Scientific reports · 2019Article
- Capturing context-specific regulation in molecular interaction networks.BMC bioinformatics · 2018Article
- Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target.Frontiers in pharmacology · 2018Article
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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