Article

Mutation-centric Network Construction using Long-Range Interactions

2026-03-18

Abstract excerpt

Somatic mutations can alter normal cells and lead to cancer development. Yet distinguishing functional driver mutations from neutral passenger mutations remains a significant challenge. Traditional genomic tools often prioritize linear overlap searches, failing to capture the complex, three-dimensional regulatory environment of the genome. We present a graph-based framework, MutationNetwork, for constructing mutat...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7fe81ef5-a64f-5f0c-8124-9b06bbae2f95
DOI: 10.64898/2026.03.16.712158

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Mutation-centric Network Construction using Long-Range InteractionsDOI 10.64898/2026.03.16.712158Read

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.Semantic proximity Read

DriverRWH: Discovering Cancer Driver Genes By Random Walk On a Gene Mutation HypergraphSemantic proximity Read

Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile.Semantic proximity Read

A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomesSemantic proximity Read

VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.Semantic proximity Read

Systematic integration of protein affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profileSemantic proximity Read

gcMECM: graph clustering of mutual exclusivity of cancer mutations.Semantic proximity Read

Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs.Semantic proximity Read

Identifying potential cancer driver genes by genomic data integration.Semantic proximity Read

EPIC: Event Prototyping via Information Constrained graph learning for personalized cancer driver gene prediction.Semantic proximity Read

ONCOPLEX: an oncology-inspired hypergraph model integrating diverse biological knowledge for cancer driver gene prediction.Semantic proximity Read

Network-aware mutation clustering of cancerSemantic proximity Read