基于CatBoost和Deep Forest的灵受体相互作用的预测以及它们在细胞间通信分析中的应用
Wei Wu1, Zhao Wang2, Longlong Liu1
1College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou 412007, China.
Journal of chemical information and modeling
|May 30, 2025
概括
细胞CDmT通过解释连接体-受体对和量化通信强度来解读细胞交叉声. 这种方法有助于了解疾病机制,并确定乳腺癌等复杂疾病的潜在治疗点.
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:
- 细胞间通信 (CCC) 对生物过程至关重要,包括生长,发育和组织形成.
- 了解细胞相互作用是解读复杂疾病和确定治疗点的关键.
- 体受体对 (LRP) 相互作用调解细胞对话,构成CCC推断的基础.
研究的目的:
- 介绍CellCDmT,一种用于阐明细胞交叉通话的新型计算方法.
- 准确地解释LRP候选者并量化LRI介导的通信强度.
- 为了可视化细胞间和细胞内通信网络,以了解疾病机制.
主要方法:
- 细胞CDmT使用PyFeat向量化LRPs,并通过XGBoost选择特征进行分类.
- 结合CatBoost和Deep Forest的整体模型对未标记的LRPs进行分类.
- 使用最大差异的三点评价策略量化通信强度,其次是可视化.
主要成果:
- 细胞CDmT在对未标记的LRPs进行分类和解码细胞交叉通话方面表现出高度准确性.
- 与现有工具进行比较证实了CellCDmT在8个评估指标上的卓越表现.
- 该方法成功地可视化了乳腺癌中的通信网络,识别了潜在的治疗点.
结论:
- 细胞CDmT为CCC推断和LRI分析提供了一个强大的计算框架.
- 已识别的LRP (MIF-CD74,WNT7B-FZD1,B2M-TFRC) 和配体 (FGF22,B2M,RSPO4) 可能在乳腺癌中具有关键作用.
- 细胞CDmT有助于理解疾病机制,促进向治疗和药物设计.
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