Quantifying tumor specificity using Bayesian probabilistic modeling for drug target discovery and prioritization.

ABSTRACT: In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening "off target" effects. A crucial first step in predicting toxicity are analyses of normal RNA and protein tissue expression, which are now possible using comprehensive molecular tissue atlases. However, no standardized approaches exist for target prioritization, which instead rely on ad-hoc thresholds and manual inspection. Such issues are compounded, given that genomic and proteomic data detection sensitivity and accuracy are often problematic. Thus, quantifiable probabilistic scores for tumor specificity that address these challenges could enable the creation of new predictive models for combinatorial drug design and correla

SUBMITTER: Li G 

PROVIDER: S-EPMC10028977 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature