CrossIsoFun:通过整合多omics数据来预测异形函数
Yiwei Liu1,2, Hong-Dong Li1,2, Jianxin Wang1,2
1School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.
Bioinformatics (Oxford, England)
|December 16, 2024
概括
由于数据集成的局限性,在计算上对基因异形函数进行注释是具有挑战性的. CrossIsoFun利用多omics数据和异形-异形相互作用来改进异形函数预测,优于现有方法.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 由替代拼接产生的基因异型可以具有不同的生物功能.
- 了解异形水平的功能对于破译基因组功能多样性至关重要.
- 当前的计算方法在整合异构的多omics数据以进行异形函数预测方面存在困难,特别是低利用异形-异形相互作用 (IIIs).
研究的目的:
- 开发一个先进的计算框架,用于准确的异形函数预测.
- 增强多omics数据集成,以改善异型级别的功能注释.
- 在预测模型中有效地纳入异形-异形相互作用 (IIIs).
主要方法:
- 介绍CrossIsoFun,一个多omics数据分析框架.
- 使用图形卷积网络 (GCNs) 进行欧米特异性分类.
- 采用视图相关性发现网络 (VCDN) 进行跨omics集成.
- 通过使用循环一致性生成对抗网络 (cycleGAN) 来从蛋白质-蛋白质相互作用 (PPI) 中生成合成III,以丰富相互作用学数据.
主要成果:
- 与最先进的方法相比,CrossIsoFun在多个数据集上表现出卓越的性能.
- 该框架有效地整合了mRNA表达,序列和PPI数据.
- 由CrossIsoFun生成的预测显示与亚细胞局部化和文献支持的异形注释一致.
结论:
- CrossIsoFun提供了一种强大而有效的方法,通过整合各种omics数据和IIIs来进行异形函数预测.
- 该框架解决了当前多学科整合方法的局限性.
- 这项工作推进了异形函数的计算预测,为基因组功能多样性提供了宝贵的见解.
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