Cell reprogramming design by transfer learning of functional transcriptional networks

arXiv:2403.04837v1 Announce Type: cross
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 cells. 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 …

abstract arxiv biology cells challenges cond-mat.dis-nn cs.lg design disease drugs functional gene knowledge machine machine learning networks next q-bio.gn q-bio.mn responses sequencing synthetic synthetic biology transfer transfer learning type