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- W3134245465 abstract "Abstract Precision medicine in oncology depends on identifying variants that are functional and interpreting them in terms of cancer disease risk, treatment guidance, and prognosis. This is especially challenging for regulatory variants, which constitute the majority of the genome and control transcription and RNA processing. We use state-of-the-art deep learning models that predict the molecular impact of variants to systematically analyze genomes and SNPs and discover BRCA-associated SNPs that impact transcriptional and post-transcriptional regulation of gene expression, identify genes and biological processes implicated across tissues of impact, and reconcile these findings with prior genomic annotation-based analysis. Citation Format: Olga Troyanskaya. Functional analysis of BRCA regulatory variants with deep learning models [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-12." @default.
- W3134245465 created "2021-03-15" @default.
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- W3134245465 date "2021-03-01" @default.
- W3134245465 modified "2023-09-27" @default.
- W3134245465 title "Abstract IA-12: Functional analysis of BRCA regulatory variants with deep learning models" @default.
- W3134245465 doi "https://doi.org/10.1158/1557-3265.adi21-ia-12" @default.
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