Article

CanBART: A Generative Foundation Model of Cancer Molecular Alterations for Synthetic Patient Generation and Genomic Profile Completion

2025-11-06

Abstract excerpt

Despite the rapid expansion of genomic profiling in oncology, real-world datasets remain limited in size and unevenly distributed, particularly for rare cancers and underrepresented patient populations. The number of available sequenced cases is often insufficient for robust machine learning, and even within existing cohorts, sequencing panels differ in gene coverage. As a result, crucial molecular features are fr...

Topics

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

Identifiers and source

Literature Corpus work: 859a1098-e333-5c1f-8352-1ba045dfb3be
DOI: 10.1101/2025.11.04.25339512

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

CanBART: A Generative Foundation Model of Cancer Molecular Alterations for Synthetic Patient Generation and Genomic Profile CompletionDOI 10.1101/2025.11.04.25339512Read

<i>In silico</i> generation of synthetic cancer genomes using generative AISemantic proximity Read

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

BioToken and BioFM – Biologically-Informed Tokenization Enables Accurate and Efficient Genomic Foundation ModelsSemantic proximity Read

A Foundation Model for the Cancer GenomeSemantic proximity Read

Phenotypic inference from sparse tumor genomes informs an explainable deep-learning model for cancer prognosisSemantic proximity Read

Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures.Semantic proximity Read

CancerVar: an Artificial Intelligence empowered platform for clinical interpretation of somatic mutations in cancerSemantic proximity Read

MutBERT: Probabilistic Genome Representation Improves Genomics Foundation ModelsSemantic proximity Read

OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision OncologySemantic proximity Read

Generalist large language models complement tailor-made predictors for tumor genomics interpretationSemantic proximity Read

Pan-cancer tumour classification and risk stratification from whole-genome somatic variants via dual-task representation learningSemantic proximity Read

GenoGlyph: Pan-cancer genomic mutation inference and risk stratification from diagnostic histopathology slidesSemantic proximity Read