Frontiers in Genetics · 2019 · 14 citations · 32 references
PharmacyPharmacologySystems BiologyDrug DesignDrug DiscoverySystems PharmacologyDrug-target Interaction MappingDrug-target Interaction NetworksMulti-target Drug DesignCompound-target Interaction PredictionComputational Target PrioritizationMachine Learning Prediction ModelsMulti-label Text ClassificationPathogenic EffectsSystems-level Drug Discovery
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).
32
ClinVar: public archive of relationships among sequence variation and human phenotype
Melissa Landrum, Jennifer M. Lee, Wonhee Jang et al. · Nucleic Acids Research · 2013 · 3.7K citations · Full text
ML-KNN: A lazy learning approach to multi-label learning
Min-Ling Zhang, Zhi‐Hua Zhou · Pattern Recognition · 2007 · 3.5K citations