乳腺癌生存预后使用图形卷积网络与Choquet模糊积分
Susmita Palmal1, Nikhilanand Arya2, Sriparna Saha2
1Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Bihar, 801106, India. susmita_2121cs34@iitp.ac.in.
Scientific reports
|September 7, 2023
概括
这项研究引入了一种使用图形卷积网络和Choquet模糊组合来预测乳腺癌存活率的新模型. 这种新的方法整合了多种学科和临床数据,以提高预后准确度.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 乳腺癌是女性的主要癌症,需要准确的预后工具.
- 以前的模型主要使用基因表达数据进行预测.
- 最近的多主题数据 (基因表达,副本数量改变) 可用性使得更全面的分析.
研究的目的:
- 开发一种用于乳腺癌预后的新型预测模型.
- 整合多omics和临床数据,以提高生存预测.
- 将乳腺癌患者分为短期和长期生存组.
主要方法:
- 使用图形卷积网络 (GCN) 来提取结构信息.
- 采用了一个Choquet模糊组合与后勤回归,随机森林和支持向量机分类器.
- 整合了Multi-omics (基因表达,拷贝数的改变) 和来自METABRIC数据库的临床数据.
主要成果:
- 拟议的模型实现了0.820.20的准确性.
- 关键性能指标包括马修斯相关系数 (0.528),精度 (0.630),灵敏度 (0.666),特异性 (0.871),平衡精度 (0.769) 和F1-测量 (0.647).
- 该模型与基线和最先进的方法相比显示出有效性.
结论:
- 开发的GCN和Choquet模糊组合模型有效预测乳腺癌患者的生存率.
- 整合多omics和临床数据显著提高预后模型的性能.
- 这种方法为个性化乳腺癌治疗策略提供了一个有希望的工具.
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