可复制和多项研究的转录组合与disint,疾病整合和聚类工具包,以及对药物重新定位的应用
Yi Cong1, Naoki Osada1, Toshinori Endo1
1Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan.
Omics : a journal of integrative biology
|February 6, 2026
概括
我们开发了disint,这是一个新的Python框架,用于整合跨疾病的大型转录组数据集. 这个工具包可以实现标准化分析,提高可重现性,并促进药物重新定位的发现.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 在各种疾病中集成大规模的转录基因数据集,由于不一致的预处理和缺乏标准化的分析框架,这带来了重大挑战.
- 现有的管道通常涉及手动参数调整和碎片化脚本,阻碍跨数据集的可比性和下游解释.
研究的目的:
- 开发一个开源的Python框架,disint (疾病集成和集群工具包),用于标准化跨数据集的转录数据集成,嵌入和集群.
- 实施原型下游模块,疾病重定位,用于提取疾病特异性基因特征并识别潜在的药物重定位候选人.
主要方法:
- 该disint框架采用了基于基因的家政规范化和特定疾病的log2折叠变化计算.
- 它具有自动化的统一多重近似和投影 (UMAP) 超参数优化和自适应的K-平均集群.
- 下游模块分析基因特征,寻找共享组件和药物重新定位潜力.
主要成果:
- 该框架在28个转录基因数据集上得到了验证,涵盖了34个疾病类别和386个样本 (255名患者,131名健康人).
- 它共处理了194,182个基因,证明了可扩展性和可重复性.
- 结果强调了该框架在提取有意义的疾病特征和探索治疗机会方面的实用性.
结论:
- 该disint框架提供了一个标准化,可复制和可扩展的解决方案,用于整合和分析跨疾病的大规模转录基因数据.
- 它的经过验证的性能突出显示了它在疾病特征分析和药物重新定位方面的翻译多功能性.
- 本工具包解决了生物医学研究中大数据集成的关键挑战.
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