可解释的生物信息学方法用于染细胞生物活性和蛋白质相互作用分析
1Burdur Mehmet Akif Ersoy University, Bucak Zeliha Tolunay School of Applied Technology and Business Administration, Department of Information Systems and Technologies, Burdur, Türkiye.
Computers in biology and medicine
|August 3, 2025
概括
这项研究使用计算方法识别了罕见的神经内分泌瘤 - - 乳色细胞瘤 (PCC) 的关键分子标和药物动机. 这些发现为开发针对MYC驱动癌症的向治疗提供了基础.
科学领域:
- 计算生物学是一种计算生物学.
- 网络药理学 网络药理学
- 在瘤学瘤学.
背景情况:
- 乳色细胞瘤 (PCC) 是一种罕见的神经内分泌瘤,由瘤性c-Myc/Max复合体驱动.
- 对于PCC的分子相互作用和向治疗方法的理解仍然很差.
研究的目的:
- 使用集成计算管道阐明PCC中的关键分子机制和生物活性动机.
- 识别关键的蛋白质标和可操作的分子特征,以开发有针对性的疗法.
主要方法:
- 综合生物信息学,网络生物学和机器学习 (随机森林,SVM,渐变增强).
- 从5000个生物活性分子 (ChEMBL) 中识别结构图案的遗传编程.
- 蛋白与蛋白相互作用 (PPI) 网络构建和分析 (STRING,BioGRID),社区检测 (Girvan-Newman) 和可解释的AI (XAI).
主要成果:
- 机器学习模型在预测生物活性方面取得了很高的准确性 (平均准确率:0.98,AUC>0.97).
- 生物活性的关键决定因素包括pIC50,分子量,脂性和结合特性.
- MYC,MAX和EP300被确定为PCC分子网络中的中心枢纽,为癌症相关途径提供了丰富的信息.
结论:
- 该研究成功地确定了色细胞瘤的关键分子标和生物活性动机.
- 这些发现为开发针对PCC和其他罕见癌症的新型向治疗提供了计算基础.
- 需要进一步的实验验证,以确认计算预测.
关键词:
生物活性的预测预测.癌症药物针对的目标是癌症药物.可解释的机器学习这是基因编程.染细胞瘤 (Pheochromocytoma) 是一种细胞瘤.蛋白质与蛋白质相互作用网络.c-Myc/Max复合体中的一个.更多相关视频
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