预测TF-目标基因协会使用异质网络和增强负性采样
Thanh Tuoi Le1,2, Xuan Tho Dang3
1Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam.
Bioinformatics and biology insights
|February 27, 2025
概括
这项研究引入了一种用于选择增强负样本的新方法,以改善转录因子 (TF) -目标基因相互作用的预测,这对于理解生物过程和疾病至关重要.
科学领域:
- 分子生物学分子生物学
- 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:
- 识别转录因子 (TF) -目标基因相互作用对于理解生物机制和疾病至关重要.
- 用于TF目标基因鉴定的实验方法往往是繁的,昂贵的,范围有限的.
- 现有的计算方法主要预测TF结合站点,而不是直接相互作用,并与强大的样本数据集构建作斗争.
研究的目的:
- 提出一种选择增强负样本的新方法,以提高预测TF-目标基因相互作用的准确性.
- 为了应对由于当前方法的负样本选择不足而导致潜在的TF目标基因关系的不完全覆盖的挑战.
主要方法:
- 在TF目标基因相互作用数据集中选择增强负样本的新战略的开发.
- 使用5倍交叉验证验证拟议方法的验证.
主要成果:
- 拟议的方法实现了0.9024 ± 0.0008.8的曲线下的高平均面积 (AUC).
- 与现有的方法相比,在预测TF-目标基因相互作用方面显著改善.
结论:
- 开发的方法通过改善负样本选择,有效地提高了TF目标基因相互作用的预测.
- 该模型具有很高的效率和准确性,在大规模的生物医学研究和数据分析中具有广泛应用的潜力.
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