一个多式模式框架,用于癌症中全面的驱动变异预测
Hai Yang1,2, Yijia Chen2, Tianyi Zhou3
1Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai, China.
Communications medicine
|November 26, 2025
概括
识别癌症驱动变体是一个挑战. 作为一种多式模式模型,ModVAR集成了DNA序列,蛋白质结构和omics数据,以准确地确定驱动器变体,帮助癌症研究和个性化治疗.
科学领域:
- 基因组学就是基因组学.
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
背景情况:
- 癌症基因组含有众多突变,但区分驱动突变和乘客突变是困难的.
- 精确识别驱动变异对于理解瘤发育和指导治疗策略至关重要.
- 当前的方法在整合各种数据类型以准确分类驾驶员变种时面临挑战.
研究的目的:
- 开发一个准确和可解释的多式联络模型来分类癌症驱动变体.
- 整合DNA序列,蛋白质3D结构和癌症omics数据以进行增强的变异分析.
- 改进针对癌症向疗法的可操作驱动突变的识别.
主要方法:
- 介绍ModVAR,一个多式模式的模型,结合了DNA序列,预测的蛋白质结构,和癌症的数据.
- 使用预先训练的模型 (DNAbert2,ESMFold) 和自主监督的方法用于omics配置文件.
- 通过使用分类指标,分子对接和对内在无序区域的分析来评估经过验证的驱动程序变体的性能.
主要成果:
- ModVAR在识别跨基准验证的癌症驱动变异方面表现出高准确性.
- 该模型优先考虑具有治疗潜力的变体,得到分子对接分析的支持.
- 结构预测有助于在内在无序的蛋白质区域中建模变异,而蛋白质结构是最有影响力的模式.
- 创建了一个公开可用的数据集,包含3,971,946个变体注释.
结论:
- ModVAR有效地集成了序列,结构和omics数据,用于驱动器变体发现.
- 该模型提供了强大的性能,有助于产生假设和发现目标,并推进了个性化癌症治疗.
- 提供了一个大规模的资源,以加速癌症研究和开发新的治疗策略.
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