ArticleFrontiers in genetics2019
Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates.
Article in Frontiers in genetics, 2019. 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, 14 citations in OpenAlex.
- Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction.BMC bioinformatics · 2024Article
- GETdb: A comprehensive database for genetic and evolutionary features of drug targets.Computational and structural biotechnology journal · 2024Article
- Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes.PNAS nexus · 2023Article
- Article
- Review
- Mining the Selective Remodeling of DNA Methylation in Promoter Regions to Identify Robust Gene-Level Associations With Phenotype.Frontiers in molecular biosciences · 2021Article
- Article
- Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation.Molecular genetics & genomic medicine · 2020Article
- Heat Diffusion Kernel Algorithm-Based Interpretation of the Disease Intervention Mechanism for DHA.Genes · 2020Article
- A Machine Learning Method for Drug Combination Prediction.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).
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