期刊
BIOCHEMISTRY
卷 60, 期 18, 页码 1430-1446出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.biochem.0c00930
关键词
-
资金
- NIH [U54HL127624 BD2K-LINCS DCIC, U24CA224260 KMC-IDG]
While many genes are associated with pain, the molecular mechanisms are largely unknown. A machine learning model was trained to predict new pain targets, showing promising results for identifying novel drug targets and pathways. This approach provides a foundation for future experimental exploration in developing safer and more effective analgesics.
While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据