Article

OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology

2026-02-19

Abstract excerpt

Somatic mutation profiling is central to cancer diagnosis and treatment selection. However, most studies focus on individual actionable mutations, overlooking the broader mutational context that shapes tumor evolution and treatment response. Here, we introduce OncoBERT, a language model that learns contextual representations of somatic mutations from large-scale clinical sequencing data spanning >210,000 patients,...

Topics

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

Identifiers and source

Literature Corpus work: e1ea0dc9-92cb-5031-bf3d-4de660299534
DOI: 10.64898/2026.02.18.706658

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision OncologyDOI 10.64898/2026.02.18.706658Read

Cancer mutational signatures representation by large-scale context embedding.Semantic proximity Read

Translating clinical gene sequencing into a foundational representation of tumor subtypeSemantic proximity Read

Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers.Semantic proximity Read

Transformer models of mutation risk at base-pair resolution identify non-coding hotspot cancer driver mutationsSemantic proximity Read

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patientsSemantic proximity Read

Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data.Semantic proximity Read

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients.Semantic proximity Read

Tumor-Origin.com: A Machine Learning Platform for Predicting Tumor Tissue of Origin from Somatic Mutation ProfilesSemantic proximity Read

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.Semantic proximity Read

MS-ConTab: multi-scale contrastive learning of mutation signatures for Pan-Cancer representation and stratification.Semantic proximity Read

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtypingSemantic proximity Read

Predictive Modeling of Novel Somatic Mutation Impacts on Cancer Prognosis: A Machine Learning Approach Using the COSMIC DatabaseSemantic proximity Read