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

DeepVRegulome：AI框架预测基因变异对人类调控组的影响

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DeepVRegulome，一个深度学习框架，旨在预测短基因变异对人类调控组的功能影响。该框架集成了464个微调的DNABERT模型，并采用定量评分、基于注意力的基序分析和生存分析来评估变异效应并将其与临床结果联系起来。与实验数据和现有预测器进行基准测试后，DeepVRegulome识别了数千个影响转录因子结合和剪接位点的突变，其中一部分与胶质母细胞瘤样本中的患者生存率相关。该框架的代码、模型和一个数据门户均公开可用。 AI

影响 该框架有望改善对非编码遗传变异的理解和优先级排序，可能有助于疾病的诊断和治疗。

排序理由 该条目描述了一个新的计算框架和相关模型，该框架和模型作为研究论文发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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DeepVRegulome：AI框架预测基因变异对人类调控组的影响

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该条目描述了一个新的计算框架和相关模型，该框架和模型作为研究论文发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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：基于DNABERT的深度学习框架，用于预测短基因组变异对人类调控组的功能影响

    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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