基于多omics特征的YY1循环的预测
Jun Ren1, Zhiling Guo2, Yixuan Qi3
1Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China; School of Mathematics and Statistics, Hainan Normal University, Haikou, China.
Methods (San Diego, Calif.)
|November 9, 2024
概括
研究人员开发了一个LightGBM模型来预测YY1循环,这对于基因调节至关重要. 该模型准确地识别了这些染色体循环,揭示了诸如转录因子联合结合等关键影响因素.
科学领域:
- 分子生物学分子生物学
- 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:
- 三维色素结构对于基因表达调节至关重要.
- 转录因子YY1促进增强剂-促进剂相互作用,类似于CTCF.
- 识别作为循环的YY1结合点仍然具有挑战性.
研究的目的:
- 使用多omics数据开发YY1-循环的预测模型.
- 确定影响YY1介导色素环形成的关键特征.
主要方法:
- 一个LightGBM机器学习模型被用于预测.
- 使用多omics数据集成来训练和测试模型.
- 精度回忆曲线下的区域 (AUPRC) 用于评估由于数据集不平衡而导致的分类器性能.
主要成果:
- 轻GBM模型表现出强大的预测性能,AUPRC ≥ 0.93.
- 该模型在训练和独立测试集 (4:1比率) 上都显示出强度和良好的性能.
- 特性重要性分析显示,CTCF,SMC3和RAD21的联合结合,以及基因组修饰和序列背景,是YY1循环形成的主要驱动因素.
结论:
- 开发的LightGBM模型有效地预测了YY1循环.
- 这项研究强调了特定的转录因子联合结合和表观遗传特征在建立由YY1.1介导的染色质环中的关键作用.
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