深度自我重建驱动的联合非负矩阵因子化模型,用于识别复杂疾病中的多个基因组成像关联
Jin Deng1, Kai Wei2, Jiana Fang1
1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.
Journal of biomedical informatics
|June 27, 2024
概括
这项研究引入了一种新的深度自我重建的联合非负矩阵因子化 (DSRJNMF) 模型,以揭示基因病理图像和转录学数据之间的多模式关联. DSRJNMF模型有效地识别了三阴性乳腺癌 (TNBC) 的成像遗传生物标志物.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 计算病理学计算病理学
- 基因组学和转录基因组学
背景情况:
- 多模式数据关联研究对于识别生物标志物和理解复杂疾病至关重要.
- 现有的方法,通常基于联合非负矩阵分解,可能无法充分利用原始数据固有的多子空间结构.
- 这种限制可能会影响后续数据整合和分析的准确性.
研究的目的:
- 提出一种新的深度自我重建的联合非负矩阵因子化 (DSRJNMF) 模型,用于增强多式联络数据关联分析.
- 结合自我表达性质用于原始数据重建,捕捉潜在的相似性结构.
- 通过稀疏性,直角性和规则化约束来整合临床先前信息,以进行生物学相关的特征选择.
主要方法:
- 开发了DSRJNMF模型,将深度自我表示与联合非负矩阵因子化集成在一起.
- 应用自我表达性质来重建原始数据,保留复杂的多次空间结构.
- 将从先前的信息中获得的稀疏性,正角性和规范化约束纳入模型.
主要成果:
- 成功地应用了DSRJNMF算法来识别三阴性乳腺癌 (TNBC) 的成像遗传关联.
- 证明了病理图像特征与miRNA基因表达之间的关联的优越估计.
- 确定了对TNBC解释至关重要的一致的多式成像遗传生物标志物.
结论:
- 拟议的DSRJNMF方法为复杂疾病的数据关联分析提供了一种新的方法.
- 这种方法有效地整合了各种数据模式,增强了生物标志物发现.
- 这些发现为指导TNBC的解释和治疗提供了宝贵的见解.
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