Cell reprogramming design by transfer learning of functional transcriptional networks.

ABSTRACT: Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cell behavior. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates to generate a model of the functional network dynamics that can be transferred to specific reprogramming goals. The approach additively combines transcriptional

SUBMITTER: Wytock TP 

PROVIDER: S-EPMC10942484 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature