發(fā)布時(shí)間：2018-04-09 00:33

  本文選題：圖聚類　切入點(diǎn)：蛋白質(zhì)復(fù)合體　出處：《控制理論與應(yīng)用》2017年06期

【摘要】：蛋白質(zhì)復(fù)合體是由兩條或多條相關(guān)聯(lián)的多肽鏈組成,在生物過程中起著重要作用.假如用圖表示蛋白質(zhì) 蛋白質(zhì)相互作用(protein-protein interactions,PPI)網(wǎng)絡(luò)數(shù)據(jù),那么從中找出緊密耦合的蛋白質(zhì)復(fù)合體是非常困難的,特別是在近年來PPI網(wǎng)絡(luò)的容量大大增加的情況下.在本文中,通過對(duì)稱非負(fù)矩陣分解,針對(duì)蛋白質(zhì)復(fù)合體檢測(cè)問題提出了一種圖聚類方法,該方法可以有效地從復(fù)雜網(wǎng)絡(luò)中檢測(cè)密集的連通子圖.并且將此方法和當(dāng)前最先進(jìn)的一些方法在3個(gè)PPI數(shù)據(jù)集中用同一個(gè)基準(zhǔn)進(jìn)行比較.實(shí)驗(yàn)結(jié)果表明,本文的方法在3個(gè)擁有不同大小和密度的數(shù)據(jù)集中均顯著優(yōu)于其它方法.
[Abstract]:Protein complex is composed of two or more associated polypeptide chains and plays an important role in biological processes.If we use graphs to represent protein-protein interactions (PPI) network data, it is very difficult to find tightly coupled protein complexes, especially when the capacity of PPI networks is greatly increased in recent years.In this paper, a graph clustering method is proposed for protein complex detection by symmetric nonnegative matrix decomposition, which can effectively detect dense connected subgraphs from complex networks.This method is compared with some of the most advanced methods in three PPI datasets using the same benchmark.The experimental results show that the proposed method is superior to other methods in three datasets with different sizes and densities.
【作者單位】： 華南師范大學(xué)計(jì)算機(jī)學(xué)院;仲愷農(nóng)業(yè)工程學(xué)院信息科學(xué)與技術(shù)學(xué)院;
【基金】：Supported by Natural Science Foundation of Guangdong Province,China(2015A030310509) National Science Foundation of China(61370229,61272067,61303049) S&T Planning Key Projects of Guangdong(2014B010117007,2015B010109003,2015A030401087,2016A030303055,2016B030305004,2016B010109008)
【分類號(hào)】：O157.5;Q51

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本文編號(hào)：1724080