articleNature BiotechnologyAug 17, 2023HYBRID OA

Predicting transcriptional outcomes of novel multigene perturbations with GEARS

YRYusuf RoohaniKHKexin HuangJLJure Leskovec

Stanford University

PubMed
Indexed incrossrefpubmed

Abstract

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene-gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing…

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Topics & keywords

Topics
Keywords
  • Gene
  • Computational biology
  • Graph
  • Perturbation (astronomy)
  • Psychological repression
  • Biology
  • Computer science
  • Gene expression
UN Sustainable Development Goals
  • Good health and well-being
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