aceInterpretable neural networks prioritize cancer driver genes from genome-wide dependency landscapes
Identifying cancer driver genes and their therapeutic impact remains a core challenge in computational cancer biology. We introduce xNNDriver and xAEDriver, two interpretable neural network frameworks that connect cancer mutations with genome-wide DepMap gene dependencies, pathway activity, and drug-response patterns. xNNDriver is a supervised pathway-guided model that evaluates whether a gene's mutation status is encoded in the genome-wide dependency landscape; we interpret model fitness as a driver potential s…
Two interpretable neural network models linked mutations to gene dependencies and recovered established cancer drivers while flagging literature-supported candidates across 3,008 genes.
The authors describe the framework as hypothesis-generating, so its prioritized drivers, pathways and therapeutic vulnerabilities require further experimental validation.
Evidence
- Peer-reviewedPLOS Computational Biology2026-10-13
How should this claim be treated?
Truvace Impact Record TRV-2026-2038, v1: “Interpretable neural networks prioritize cancer driver genes from genome-wide dependency landscapes.” Truvace, 2026-10-20. /record/TRV-2026-2038 (accessed at citation time). sha256 f9a95fda07df0ded…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2038 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.