基于网络嵌入和一类分类的功能基因和疾病基因预测
Weiyu Shi1, Yan Zhang2, Yeqing Sun3
1College of Maritime Economics and Management, Dalian Maritime University, Dalian, 116026, China.
Interdisciplinary sciences, computational life sciences
|September 4, 2024
概括
本研究介绍了VGAEMCD,这是一种新的机器学习方法,用于使用网络嵌入和一类分类来预测疾病和功能基因. 在不需要负面示例的情况下,VGAEMCD准确地识别了基因,超过了现有的算法.
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:
- 预测疾病和功能基因对于理解生物系统至关重要.
- 现有的方法因缺乏负面示例而困难,仅依赖已知的正基因组.
研究的目的:
- 开发一个强大的机器学习算法,用于统一的疾病和功能基因预测.
- 为了应对基因功能和疾病关联研究中有限的负面示例的挑战.
主要方法:
- 使用实验验验证的基因构建了一个蛋白质与蛋白质相互作用 (PPI) 网络.
- 使用变量图自动编码器 (VGAE) 嵌入网络以表示基因.
- 利用一个改进的深度学习一类分类器 (快速最小协差决定器,快速-MCD) 进行基因预测.
主要成果:
- VGAEMCD成功地以统一的方式预测功能基因和疾病基因.
- 该算法在回忆,精度,F测量,特异性和准确性方面实现了高性能.
- 与经典的一类分类和最先进的预测算法相比,VGAEMCD表现出卓越的性能.
结论:
- VGAEMCD有效地学习了正基因样本的分布特征.
- 拟议的方法准确地识别了新功能和与疾病相关的基因.
- VGAEMCD为基因功能和疾病预测提供了强大的工具,而不需要负数据或表达特征.
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