基于潜伏子空间学习的癌症亚型的多omics聚类
Xiucai Ye1, Yifan Shang2, Tianyi Shi3
1Department of Computer Science, University of Tsukuba, Tsukuba, 3058577, Japan; Tsukuba Life Science Innovation Program, University of Tsukuba, Tsukuba, 3058577, Japan.
Computers in biology and medicine
|July 25, 2023
概括
一种名为MCLS (多omics聚类) 的新方法有效地使用部分多omics数据识别癌症亚型. 这种方法处理缺少的数据,改进了现有的生物机制发现方法.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 高通量技术使得多主题数据分析能够理解疾病病因学.
- 现有的多omics集群的计算方法经常与不完整的数据集 (部分多omics) 斗争.
- 准确的癌症亚型识别对于了解疾病机制和开发向疗法至关重要.
研究的目的:
- 开发一种新的计算方法,用于集群部分多omics数据.
- 为了解决癌症亚型识别的多奥米克分析中缺少数据的挑战.
- 通过使用多omics数据,提高癌症亚型发现的效率和有效性.
主要方法:
- 提出了一种基于潜潜子空间学习 (MCLS) 的新型多omics集群方法.
- 利用主要组件分析 (PCA) 和奇点值分解 (SVD) 来从完整的奥米克数据中构建一个潜在的子空间.
- 将不完整的多omics数据投射到潜子空间,并应用光谱聚类来进行样本分组.
主要成果:
- 在7个癌症数据集中,MCLS在癌症亚型识别方面表现出卓越的效率和有效性.
- 该方法成功处理了部分多omics数据,超过了最先进的方法.
- 实验结果验证了MCLS在分析缺失值的多omics数据方面的能力.
结论:
- 拟议的MCLS方法为癌症研究中的部分多omics数据集群提供了强大的解决方案.
- MCLS有助于更全面地了解癌症及其潜在的生物机制.
- 该方法为识别不同癌症亚型提供了宝贵的见解,有助于个性化医学方法.
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