和聲搜索聚類優(yōu)化模型的PPI功能模塊挖掘算法研究
發(fā)布時間：2019-01-19 11:56
【摘要】：蛋白質(zhì)交互(Protein-Protein Interaction,PPI)網(wǎng)絡(luò)是生物體內(nèi)蛋白質(zhì)之間相互作用形成的網(wǎng)絡(luò),在拓?fù)浣Y(jié)構(gòu)上呈現(xiàn)小世界特性和無尺度特性,屬于復(fù)雜網(wǎng)絡(luò)的一種。近年來,隨著高通量技術(shù)的發(fā)展,可獲得的蛋白質(zhì)交互數(shù)據(jù)日漸豐富,基于蛋白質(zhì)交互網(wǎng)絡(luò)的功能模塊挖掘有助于預(yù)測未知蛋白質(zhì)功能,為疾病研究提供理論基礎(chǔ),已成為生物信息學(xué)領(lǐng)域新的研究熱點。與此同時,智能算法由于在解決復(fù)雜問題方面的優(yōu)越性獲得了廣泛的應(yīng)用,基于智能計算的算法被陸續(xù)應(yīng)用在蛋白質(zhì)交互網(wǎng)絡(luò)的功能模塊挖掘問題上,逐漸成為新的研究熱點。本文將和聲搜索算法應(yīng)用在蛋白質(zhì)交互網(wǎng)絡(luò)的功能模塊挖掘問題上并進(jìn)行了深入的研究,主要工作包括:(1)基于和聲搜索算法,提出了基于和聲搜索(Harmony Search,HS)聚類優(yōu)化模型的蛋白質(zhì)交互網(wǎng)絡(luò)功能模塊挖掘算法(HMS-FMD),算法改進(jìn)了傳統(tǒng)和聲搜索的搜索策略,在蛋白質(zhì)交互網(wǎng)絡(luò)中,將搜索聚集系數(shù)較大的結(jié)點集合作為算法的目標(biāo)函數(shù)。通過實驗對算法的參數(shù)進(jìn)行分析和對比,得到了算法參數(shù)的最優(yōu)設(shè)置,與其他挖掘算法相比,實驗結(jié)果表明本文算法能有效挖掘出蛋白質(zhì)交互網(wǎng)絡(luò)中的功能模塊。(2)當(dāng)前的研究普遍將蛋白質(zhì)網(wǎng)絡(luò)看作一個邊存在確定性的無向圖,但由于高通量生物檢測技術(shù)對蛋白質(zhì)交存檢測存在固有的誤差,因此實驗測得的蛋白質(zhì)是否真實存在交互性是不確定的。在不確定圖數(shù)據(jù)挖掘問題上,蛋白質(zhì)功能模塊挖掘問題的計算復(fù)雜性通常要比確定圖數(shù)據(jù)同一挖掘問題的計算復(fù)雜性要高。本文利用“可能世界”模型,在不確定性蛋白質(zhì)交互網(wǎng)絡(luò)的基礎(chǔ)上,提出了基于和聲搜索優(yōu)化模型的不確定蛋白質(zhì)交互網(wǎng)絡(luò)功能模塊挖掘算法,通過理論推導(dǎo),簡化了計算復(fù)雜度,使用和聲搜索聚類優(yōu)化模型,將期望密度較大的結(jié)點集合作為算法搜索的目標(biāo)函數(shù)。通過實驗對算法進(jìn)行分析和對比,結(jié)果表明該算法具有較好的聚類結(jié)果。本文通過對和聲搜索聚類優(yōu)化模型的算法研究,并應(yīng)用在蛋白質(zhì)交互網(wǎng)絡(luò)功能模塊挖掘問題上,在一定程度上豐富了蛋白質(zhì)交互網(wǎng)絡(luò)功能模塊挖掘算法的理論研究,為蛋白質(zhì)交互網(wǎng)絡(luò)的功能模塊挖掘研究提供一定的理論指導(dǎo)。
[Abstract]:Protein interaction (Protein-Protein Interaction,PPI) network is a network formed by the interaction of proteins in organisms. It has the characteristics of small world and no scale in topological structure. It belongs to one of the complex networks. In recent years, with the development of high-throughput technology, more and more protein interaction data are available. The function module mining based on protein interaction network is helpful to predict the unknown protein function and provide the theoretical basis for disease research. It has become a new research hotspot in the field of bioinformatics. At the same time, due to the advantages of intelligent algorithms in solving complex problems, intelligent computing algorithms have been gradually used in the protein interactive network functional module mining problem, gradually becoming a new research hotspot. In this paper, the harmonic search algorithm is applied to the functional module mining problem of protein interactive network. The main work is as follows: (1) based on the harmony search algorithm, the (Harmony Search, based on harmony search is proposed. HS) clustering optimization model of protein interactive network functional module mining algorithm (HMS-FMD), the algorithm improved the traditional harmony search strategy, in protein interactive networks, The set of nodes with large searching aggregation coefficient is regarded as the objective function of the algorithm. Through the analysis and comparison of the parameters of the algorithm, the optimal setting of the algorithm parameters is obtained, which is compared with other mining algorithms. Experimental results show that the proposed algorithm can effectively mine the functional modules in protein interaction networks. (2) the current research generally regards protein networks as an undirected graph with deterministic edge. However, due to the inherent error of high-throughput biological detection technology for protein deposit detection, it is uncertain whether the protein measured by experiments is truly interactive or not. In the problem of uncertain graph data mining, the computational complexity of protein functional module mining problem is usually higher than that of determining the same mining problem of graph data. In this paper, based on the uncertain protein interaction network and the "possible world" model, an algorithm for mining the functional modules of uncertain protein interaction networks based on harmony search optimization model is proposed. The computational complexity is simplified and the optimal model of harmonic search clustering is used. The set of nodes with high expected density is taken as the objective function of the algorithm. The experimental results show that the algorithm has better clustering results. In this paper, the algorithm of clustering optimization model of harmony search is studied, and it is applied to the problem of protein interactive network functional module mining. To some extent, it enriches the theoretical research of protein interactive network functional module mining algorithm. It provides some theoretical guidance for the research of functional module mining of protein interactive network.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：Q51;TP311.13
[Abstract]:Protein interaction (Protein-Protein Interaction,PPI) network is a network formed by the interaction of proteins in organisms. It has the characteristics of small world and no scale in topological structure. It belongs to one of the complex networks. In recent years, with the development of high-throughput technology, more and more protein interaction data are available. The function module mining based on protein interaction network is helpful to predict the unknown protein function and provide the theoretical basis for disease research. It has become a new research hotspot in the field of bioinformatics. At the same time, due to the advantages of intelligent algorithms in solving complex problems, intelligent computing algorithms have been gradually used in the protein interactive network functional module mining problem, gradually becoming a new research hotspot. In this paper, the harmonic search algorithm is applied to the functional module mining problem of protein interactive network. The main work is as follows: (1) based on the harmony search algorithm, the (Harmony Search, based on harmony search is proposed. HS) clustering optimization model of protein interactive network functional module mining algorithm (HMS-FMD), the algorithm improved the traditional harmony search strategy, in protein interactive networks, The set of nodes with large searching aggregation coefficient is regarded as the objective function of the algorithm. Through the analysis and comparison of the parameters of the algorithm, the optimal setting of the algorithm parameters is obtained, which is compared with other mining algorithms. Experimental results show that the proposed algorithm can effectively mine the functional modules in protein interaction networks. (2) the current research generally regards protein networks as an undirected graph with deterministic edge. However, due to the inherent error of high-throughput biological detection technology for protein deposit detection, it is uncertain whether the protein measured by experiments is truly interactive or not. In the problem of uncertain graph data mining, the computational complexity of protein functional module mining problem is usually higher than that of determining the same mining problem of graph data. In this paper, based on the uncertain protein interaction network and the "possible world" model, an algorithm for mining the functional modules of uncertain protein interaction networks based on harmony search optimization model is proposed. The computational complexity is simplified and the optimal model of harmonic search clustering is used. The set of nodes with high expected density is taken as the objective function of the algorithm. The experimental results show that the algorithm has better clustering results. In this paper, the algorithm of clustering optimization model of harmony search is studied, and it is applied to the problem of protein interactive network functional module mining. To some extent, it enriches the theoretical research of protein interactive network functional module mining algorithm. It provides some theoretical guidance for the research of functional module mining of protein interactive network.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：Q51;TP311.13
【參考文獻(xiàn)】
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1 郭琦;盧意力;李s，
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