<b>HyperMap: An Efficient Meta-Learning Framework for Transferring Perturbation Responses Across Diverse Biological Contexts</b>
dhaka, bhavya;gao, jiahao;ideker, trey
Description
HyperMapDB is a unified resource of predicted transcriptional responses to 19,036 genetic and chemical perturbations across 19 human cell lines. Predictions were generated using HyperMap, a meta-learning framework that transfers perturbation response knowledge from existing atlases to new biological contexts. The dataset contains predicted single-cell gene expression deltas for 2,500 genes.
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Publication Details
DOI
Publisher
figshare
Subfield
Molecular Biology
Field
Biochemistry, Genetics and Molecular Biology
Domain
Life Sciences
Confidence Score
50%
Source
Scholar Data Model
Keywords
Deep learningGene expression (incl. microarray and other genome-wide approaches)