Back to search

Article

Cross-gene kinase domain hotspot analysis with AI pathogenicity prediction identifies FGFR2 D650G as a candidate actionable variant

2026-07-03

Abstract excerpt

<title>Abstract</title> <p>Variants of unknown significance (VUSs) constitute most variants detected by cancer genomic profiling, limiting the clinical application of precision oncology. We developed a systematic framework that integrates cross-gene mutation mapping across 41 receptor tyrosine kinase genes with AI-driven pathogenicity prediction. By mapping 32,462 kinase-domain mutations from the AACR Project GEN...

Topics

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

Identifiers and source

Literature Corpus work
78a8467a-4dae-52b0-9d8a-e646ff7d51c0
DOI
10.21203/rs.3.rs-10221428/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Cross-gene kinase domain hotspot analysis with AI pathogenicity prediction identifies FGFR2 D650G as a candidate actionable variantDOI 10.21203/rs.3.rs-10221428/v1
Select a neighboring publication to make it the new centre.