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Microarray sampling and network modeling for drug toxicity prediction
Microarray sampling and network modeling for drug toxicity prediction
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机译:用于药物毒性预测的微阵列采样和网络建模
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摘要
A computational systems pharmacology framework consisting of microarray sampling of gene expression and metabolic data, statistical modeling and machine learning based on comprehensive integration of systems biology data, including drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations, and reported drug side effects, can predict drug toxicity or drug adverse reactions (ADRs). A disease-specific pathway model is first constructed with proteins and drugs important to the disease by using computational connectivity maps (C-Maps). Through the pathway model-based ranking algorithm, ideal drugs or optimized drug combination can be discovered for a patient to modulate the gene expression profile of this patient close to those in healthy individuals at pathway-level.
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