基于统计和深度学习的多omics集成对乳腺癌亚型分类的比较分析
Mahmoud M Omran1,2,3, Mohamed Emam1,4,5, Mariam Gamaleldin3
1Bioinformatics Group, Center for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt.
Journal of translational medicine
|July 2, 2025
概括
多种omics的整合改善了乳腺癌 (BC) 的亚型. 统计MOFA+方法在特征选择方面比深度学习MOGCN更有效,识别了个性化医学的关键途径.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 乳腺癌 (BC) 是全球癌症死亡的主要原因.
- BC亚型的异质性使分子理解,诊断和治疗变得复杂.
- 多omics集成显示增强BC亚型识别的承诺,但方法需要评估.
研究的目的:
- 为了比较统计 (MOFA+) 和深度学习 (MOGCN) 多omics集成方法用于乳腺癌亚型.
- 评估特征选择的有效性和BC亚型的生物相关性.
- 确定推进个性化乳腺癌医学的最佳方法.
主要方法:
- 综合宿主转录基因组学,表观基因组学和枪微生物组数据来自960BC患者样本.
- 将MOFA+ (统计) 和MOGCN (深度学习) 进行比较,以实现多主题集成.
- 使用线性/非线性模型评估特征歧视,并分析路径相关性.
主要成果:
- 在特征选择方面,MOFA+的表现优于MOGCN,在非线性分类中获得更高的F1得分 (0.75).
- MOFA+确定了121条相关途径,而MOGCN确定了100条.
- 关键的途径,如Fc玛R介导的细胞分裂和SNARE途径,涉及,提供了对免疫反应和瘤进展的见解.
结论:
- MOFA+是一种优质的无监督工具,用于乳腺癌亚型的特征选择.
- 多omics集成具有改善BC亚型预测的巨大潜力.
- 这些发现为开发针对乳腺癌的个性化药物策略提供了关键的见解.
关键词:
乳腺癌是什么? 乳腺癌是什么在F1中,F1的分数是什么?Fc玛R介导的细胞分裂MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA + MOFA在MoGCN中,你会发现.多领域的整合.网络分析 网络分析个性化医疗是个性化的医疗.通过SNARE路径.更多相关视频
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