【摘要】：基因之間存在多種多樣的表達調(diào)控活動,一般認(rèn)為這些調(diào)控關(guān)系隱含在基因表達譜中。因此,可以根據(jù)基因表達數(shù)據(jù)對基因調(diào)控狀態(tài)進行建模,以挖掘具有生物學(xué)意義的信息及隱含在其中的基因調(diào)控關(guān)系。本文分別利用獨立成分分析(ICA)和非負(fù)矩陣分解(NMF)這兩種無監(jiān)督矩陣分解技術(shù)對阿爾茨海默病(AD)基因表達數(shù)據(jù)進行顯著基因提取及基因調(diào)控網(wǎng)絡(luò)的構(gòu)建,通過生物學(xué)分析,探討了兩種不同矩陣分解技術(shù)在挖掘潛在致病基因上的作用,通過結(jié)合兩種方法所提取的顯著基因的生物學(xué)分析,體現(xiàn)了炎癥反應(yīng)在AD致病機制中的重要作用,為AD早期診斷、致病機制研究及基因生物標(biāo)志物的探尋提供了有益的方法。
[Abstract]:There are a variety of expression and control activities among the genes, which are generally thought to be implied in the gene expression profile. Therefore, the gene regulation state can be modeled according to the gene expression data, so that the information with the biological meaning and the gene regulation relation hidden therein can be excavated. In this paper, two non-supervised matrix decomposition techniques of independent component analysis (ICA) and non-negative matrix decomposition (NMF) were used to study the expression data of Alzheimer's disease (AD) gene. The effects of two different matrix decomposition techniques on the potential pathogenic genes are discussed, and the important role of the inflammatory reaction in the pathogenesis of AD is shown by the biological analysis of the significant genes extracted by combining the two methods, which is the early diagnosis of AD. The research of pathogenic mechanism and the search of genetic biomarkers provide a useful method.
【作者單位】： 上海海事大學(xué)信息工程學(xué)院;美國羅文大學(xué)醫(yī)藥研究中心;
【基金】：國家自然科學(xué)基金資助項目(61271446) 上海市科委青年科技啟明星計劃(A類)資助項目(11QA1402900) 上海市教委科研創(chuàng)新項目資助(11YZ141)
【分類號】：R749.16
，

本文編號：2511888