Quantized multi-task learning for context-specific representations of gene network dynamics

Abstract excerpt

While often represented as static entities, gene networks are highly context-dependent. Here, we developed a multi-task learning strategy to yield context-specific representations of gene network dynamics. We assembled a corpus comprising ~103 million human single-cell transcriptomes from a broad range of tissues and diseases and performed a two stage pretraining, first with non-malignant cells to generate a found...

Topics

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• Colorectal Neoplasms
• Epithelial-Mesenchymal Transition
• Gene Expression Regulation, Neoplastic
• Gene Regulatory Networks
• Oncogenes
• Single-Cell Analysis
• Single-Cell Gene Expression Analysis
• Transcriptome

Identifiers and source

Literature Corpus work

f8df2fdb-cd67-5406-8dc7-3208fb91f3bc

DOI

10.1101/2024.08.16.608180