ArticleProteins2008
An integrated approach to inferring gene-disease associations in humans.
Article in Proteins, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
65 citing papers in PubMed.
- Disease candidate genes prediction using positive labeled and unlabeled instances.BMC medical genomics · 2025Article
- Autophagy dark genes: Can we find them with machine learning?Natural sciences (Weinheim, Germany) · 2023Article
- Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.Communications biology · 2022Article
- Angiogenesis goes computational - The future way forward to discover new angiogenic targets?Computational and structural biotechnology journal · 2022Review
- Identification of infectious disease-associated host genes using machine learning techniques.BMC bioinformatics · 2019Article
- Drug repositioning based on individual bi-random walks on a heterogeneous network.BMC bioinformatics · 2019Article
- Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge.Human mutation · 2019Article
- Disease gene prediction for molecularly uncharacterized diseases.PLoS computational biology · 2019Article
- Article
- The research on gene-disease association based on text-mining of PubMed.BMC bioinformatics · 2018Article
- Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.PloS one · 2018Article
- Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.PloS one · 2018Article
- Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm.Scientific reports · 2017Article
- Is the average shortest path length of gene set a reflection of their biological relatedness?Journal of bioinformatics and computational biology · 2016Article
- KATZLDA: KATZ measure for the lncRNA-disease association prediction.Scientific reports · 2015Article
- Improved detection of disease-associated variation by sex-specific characterization and prediction of genes required for fertility.Andrology · 2015Article
- Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.PLoS computational biology · 2015Article
- Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model.Scientific reports · 2015Article
- Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.FEBS open bio · 2015Article
- KNGP: A network-based gene prioritization algorithm that incorporates multiple sources of knowledge.American journal of bioinformatics and computational biology · 2015Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
unlabelledOne of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene-disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously. AVAILABILITY: www.phenopred.org.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.