TGPred:通过整合统计,机器学习和优化来预测转录因子的目标基因的高效方法
Xuewei Cao1, Ling Zhang2,3, Md Khairul Islam2,3
1Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA.
NAR genomics and bioinformatics
|September 15, 2023
概括
新的统计方法准确地识别了转录因子 (TF) -目标基因 (TG) 对和基因调控网络 (GRNs). 这些方法利用弹性网和基于网络的惩罚,提供更快的计算和更好的全基因组预测准确性.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 准确推断转录因子 (TF) -目标基因 (TG) 相互作用对于理解基因调节至关重要.
- 目前用于全基因组TG预测和通路基因调节网络 (GRN) 推断的现有方法存在局限性.
研究的目的:
- 开发和评估新的统计方法来推断TF-TG对和通路GRNs.
- 提高基因调节分析的计算方法的速度和准确性.
主要方法:
- 开发了四种TF-TG推断方法,将MSE或Huber损失与ENET或Lasso处罚相结合.
- 开发了两条通路的GRN推断方法,将Huber或MSE损失与基于网络的惩罚结合起来.
- 利用加速近接梯度下降 (APGD) 算法进行高效的参数优化.
主要成果:
- 基于ENET的TF-TG方法在合成数据上优于基于Lasso的方法.
- 基于网络的方法 (Huber-Net,MSE-Net) 在路径GRN推断方面表现优越.
- 胡伯-ENET和MSE-ENET使用转录基因数据证明了全基因组TF-TG预测的高准确性.
结论:
- 开发的TF-TG鉴定方法解决了对TF-TG相互作用的全基因组预测和验证的需求.
- 基于网络的方法是预测路径GRN的有效工具.
- 这些新的方法在计算基因调节网络分析中提供了更高的速度和准确性.
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