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Article

MMSplice: modular modeling improves the predictions of genetic variant effects on splicing.

Genome biology - 1 Mar 2019

Cheng Jun, Nguyen Thi Yen Duong, Cygan Kamil J, Çelik Muhammed Hasan, Fairbrother William G, Avsec Žiga, Gagneur Julien

Abstract excerpt

Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping prediction challenge. The MMSplice modules are neural networks scoring exon, intron, and splice sites, trained on distinct large-scale genomics datasets. These modules are combined to predict...

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Identifiers

PMID: 30823901
DOI: 10.1186/s13059-019-1653-z
PMCID: PMC6396468

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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