使用多模式生物医学语言模型预测疾病特异性变体的病原性
Yilin Liu1,2, David N Cooper3, Haiyuan Yu1,2
1Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
bioRxiv : the preprint server for biology
|September 26, 2025
概括
疾病特异性变异性致病性预测 (DIVA) 是一种新的深度学习框架. DIVA准确地预测疾病类型和变种有害性,改善遗传疾病诊断.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 医疗保健中的机器学习
背景情况:
- 误解变异对于遗传疾病诊断和风险预测至关重要.
- 目前的方法缺乏疾病特定的背景,限制了临床效用.
- 准确的误解变体的解释需要疾病特异性病原性预测.
研究的目的:
- 引入一个新的深度学习框架,DIVA (疾病特异变体致病性预测).
- 预测错误变体的疾病类型和有害性概率.
- 通过结合疾病特异性来增强临床变异解释.
主要方法:
- DIVA集成了蛋白质序列和与疾病相关的文本注释.
- 使用两个预训练的语言模型进行信息编码.
- 采用对比式学习模式,将变体与相关疾病对齐.
主要成果:
- 在疾病预测准确性方面,DIVA的表现优于现有的基线方法.
- 对于错误变体的临床策划疾病注释来说,它具有很高的相关性.
- 纳入AlphaMissense得分可以提高变体有害性预测.
结论:
- DIVA提供了准确的,特定于疾病的变异性致病性预测.
- 解决了在临床环境中对上下文感知变体解释的未满足需求.
- 提供了对预测变种病原性与疾病意识的新见解.
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