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DeepVRegulome: AI framework predicts genomic variant impact on human regulome

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DeepVRegulome, a deep-learning framework designed to predict the functional impact of short genomic variants on the human regulome. This framework integrates 464 fine-tuned DNABERT models and employs quantitative scoring, attention-based motif analysis, and survival analysis to assess variant effects and link them to clinical outcomes. Benchmarked against experimental data and existing predictors, DeepVRegulome identified thousands of mutations affecting transcription factor binding and splice sites, with a subset linked to patient survival in glioblastoma samples. The framework's code, models, and a data portal are publicly available. AI

IMPACT This framework could improve the understanding and prioritization of non-coding genetic variants, potentially aiding in the diagnosis and treatment of diseases.

RANK_REASON The item describes a new computational framework and associated models published as a research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepVRegulome: AI framework predicts genomic variant impact on human regulome

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The item describes a new computational framework and associated models published as a research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Pratik Dutta, Matthew Obusan, Rekha Sathian, Max Chao, Pallavi Surana, Nimisha Papineni, Yanrong Ji, Zhihan Zhou, Han Liu, Alisa Yurovsky, Ramana V Davuluri ·

    DeepVRegulome: DNABERT-based deep-learning framework for predicting the functional impact of short genomic variants on the human regulome

    arXiv:2511.09026v2 Announce Type: replace-cross Abstract: Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood. Despite recent advances in deep-learning genomic approaches, accurately predicting and priori…

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