通过数据平衡和表示学习增强转录因子监管网络分析
1Academy of Policy and Development, Hanoi, Hanoi, 100000, VIET NAM.
Biomedical physics & engineering express
|February 5, 2026
概括
这项研究引入了一种新的计算方法,可以准确预测转录因子 (TF) 和目标基因相互作用,克服数据不平衡问题,以改善癌症研究和药物发现.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 转录因子 (TF) 和基因相互作用对基因调节至关重要,并与癌症等疾病有关.
- 用于TF目标预测的实验方法昂贵,缺乏可扩展性.
- 机器学习模型中的数据不平衡阻碍了TF-target相互作用的准确预测.
研究的目的:
- 开发一种新的计算框架,用于更好地预测转录因子-向基因相互作用.
- 为了应对TF-target预测模型中数据不平衡的挑战.
- 提高生物医学应用TF-目标相互作用预测的准确性和通用性.
主要方法:
- 整合K-means++集群与数据平衡策略,以减轻偏向低频TF的偏差.
- 应用深度学习技术,包括随机步行采样和跳过图形嵌入,用于生物网络表示.
- 使用五重交叉验证方法来评估模型性能.
主要成果:
- 拟议的方法显著提高了TF-目标相互作用预测的准确性.
- 实现了0.9452 ± 0.0047.7的曲线下面面积 (AUC) 的平均优势.
- 通过有效地解决数据不平衡,证明了改进的模型通用化.
结论:
- 这种新型框架为准确的TF-target交互预测提供了强大的解决方案,克服了数据不平衡的挑战.
- 这种方法对推进分子生物学和生物医学研究具有重大意义.
- 促进TF目标基因的发现,并有助于开发新的治疗策略.
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