ArticleBioinformatics (Oxford, England)2016
A knowledge-based approach for predicting gene-disease associations.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- Graph Artificial Intelligence in Medicine.Annual review of biomedical data science · 2024Review
- KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.Briefings in bioinformatics · 2024Article
- A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease.Bioinformatics and biology insights · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine.Frontiers in genetics · 2022Review
- Identifying potential association on gene-disease network via dual hypergraph regularized least squares.BMC genomics · 2021Article
- A network-based machine-learning framework to identify both functional modules and disease genes.Human genetics · 2021Article
- Graph Neural Networks and Their Current Applications in Bioinformatics.Frontiers in genetics · 2021Article
- A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis.Genes · 2020Article
- MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.Molecular pharmaceutics · 2020Article
- Differential kinase activity of ACVR1 G328V and R206H mutations with implications to possible TβRI cross-talk in diffuse intrinsic pontine glioma.Scientific reports · 2020Article
- Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery.BMC bioinformatics · 2020Article
- Context-Specific Coordinately Regulatory Network Prioritize Breast Cancer Genetic Risk Factors.Frontiers in genetics · 2020Article
- Prioritizing target-disease associations with novel safety and efficacy scoring methods.Scientific reports · 2019Article
- A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.PloS one · 2019Article
- Heterogeneous network embedding for identifying symptom candidate genes.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes.Bioinformatics (Oxford, England) · 2018Article
- Automatic extraction of gene-disease associations from literature using joint ensemble learning.PloS one · 2018Article
- Large-scale analysis of disease pathways in the human interactome.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
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2 authors.
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Abstract
motivationRecent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat deadly diseases and provide possible targets to cure these diseases. Genome-wide association studies (GWAS) have identified many individual genes associated with common diseases. To exploit the large amount of data obtained from GWAS studies and leverage our understanding of common as well as rare diseases, we have developed a knowledge-based approach to predict gene-disease associations. We first derive gene-gene mutual information by utilizing the cooccurrence of genes in known gene-disease association data. Subsequently, the mutual information is combined with known protein-protein interaction networks by a boosted tree regression method.
resultsThe method called Know-GENE is compared with the method of random walking on the heterogeneous network using the same input data. For a set of 960 diseases, using the same training data in testing in 3-fold cross-validation, the average recall rate within the top ranked 100 genes by Know-GENE is 65.0% compared with 37.9% by the state of the art random walking on heterogeneous network. This significant improvement is mostly due to the inclusion of knowledge-based mutual information. AVAILABILITY AND IMPLEMENTATION: Predictions for genes associated with the 960 diseases are available at http://cssb2.biology.gatech.edu/knowgene CONTACT: : skolnick@gatech.edu.
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