Prospective update of a systematic review of experimentally validated machine-learning-prioritized therapeutic targets

Yuanzhi He

This prospective update will evaluate peer-reviewed studies in which machine-learning or related data-driven inference methods prioritize therapeutic targets for human disease and the prioritized targets undergo independent experimental validation. The update will use expanded multi-database searches, independent duplicate screening and data extraction, a prespecified validation hierarchy, and an exploratory random-effects synthesis of candidate-level validation yield when methodologically appropriate. Work completed before this registration was used to assess feasibility and refine operational definitions. Following registration, the literature will be searched afresh and all eligible records will be re-screened and re-extracted under the registered methods; any deviations will be documented transparently.