Tianjie Chen, Md Faisal Kabir
Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data
PloS one
review explores transformative impact machine learning ml carcinogenicity prediction within drug development discusses historical context recent advancements emphasizing significance ml methodologies overcoming challenges related data interpretation ethical considerations regulatory acceptance review comprehensively examines integration ml deep learning diverse artificial intelligence ai approaches various aspects drug development safety assessments explores applications ranging earlyphase compound screening clinical trial optimization highlighting versatility ml enhancing predictive accuracy efficiency analysis traditional approaches vivo rodent bioassays vitro assays review underscores limitations resource intensity associated methods provides expert insights ml offers innovative solutions address challenges revolutionizing safety assessments drug development