CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.

ABSTRACT: CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, achieving reliable therapeutic effects with improved safety and efficacy requires informed target gene selection. This depends on a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) that regulate cell phenotype and function. Machine learning models have been previously used for GRN reconstruction using RNA-seq data, but current techniques are limited to single cell types and focus mainly on transcription factors. This restriction overlooks many potential CRISPR target genes, such as those encoding extracellular matrix components, growth factors, and signaling molecules, thus limiting the applicability of these models for CRI

SUBMITTER: Graham JP 

PROVIDER: S-EPMC11244939 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature