【摘要】：基因調(diào)控網(wǎng)絡(luò)是指細(xì)胞內(nèi)基因之間的相互作用形成的調(diào)控網(wǎng)絡(luò),它是生物體內(nèi)控制基因表達(dá)的主要機(jī)制。構(gòu)建基因調(diào)控網(wǎng)絡(luò)是理解生命活動(dòng)本質(zhì)的重要手段之一,因此利用高通量實(shí)驗(yàn)數(shù)據(jù),特別是微陣列數(shù)據(jù)構(gòu)建基因調(diào)控網(wǎng)絡(luò)已成為系統(tǒng)生物學(xué)領(lǐng)域的研究熱點(diǎn)。但目前已有的基于微陣列的基因調(diào)控網(wǎng)絡(luò)構(gòu)建方法大多存在著無法確定調(diào)控方向或計(jì)算復(fù)雜度過高等問題,因此,本文提出了一種結(jié)合已有的相關(guān)性檢驗(yàn)方法和常微分方程建模方法的基因調(diào)控網(wǎng)絡(luò)構(gòu)建方法。該方法首先利用擾動(dòng)實(shí)驗(yàn)數(shù)據(jù)計(jì)算基因之間的皮爾森相關(guān)系數(shù),然后通過Z分?jǐn)?shù)排序的方法構(gòu)建一個(gè)初始的基因調(diào)控網(wǎng)絡(luò)。在此基礎(chǔ)上,使用時(shí)間序列數(shù)據(jù)和常微分方程建模的方法對(duì)這個(gè)初始調(diào)控網(wǎng)絡(luò)進(jìn)行優(yōu)化。在常微分方程模型建立之后,基因網(wǎng)絡(luò)推導(dǎo)問題就轉(zhuǎn)變成了一個(gè)模型參數(shù)估計(jì)問題。本文提出了一種內(nèi)嵌禁忌搜索的粒子群優(yōu)化算法,即禁忌粒子群優(yōu)化來估計(jì)模型參數(shù)。在進(jìn)行模型參數(shù)估計(jì)時(shí),為了降低計(jì)算復(fù)雜性,首先利用曲線擬合方法對(duì)時(shí)間序列表達(dá)譜數(shù)據(jù)進(jìn)行曲線擬合,估計(jì)出每一個(gè)時(shí)間點(diǎn)的微分,這樣微分方程的參數(shù)估計(jì)問題就轉(zhuǎn)變?yōu)橐粋�(gè)偽多元線性回歸的問題,計(jì)算時(shí)間大大降低。最后,我們使用標(biāo)準(zhǔn)測(cè)試集和真實(shí)的微陣列數(shù)據(jù)對(duì)本文所提出的方法進(jìn)行了實(shí)驗(yàn)驗(yàn)證。結(jié)果表明,本文所提出的算法在構(gòu)建基因調(diào)控網(wǎng)絡(luò)時(shí),識(shí)別的敏感性、特異性和精確性等方面均較已有方法有所提高,同時(shí),本文方法的計(jì)算速度也較已有方法快。
[Abstract]:Gene regulatory network is a regulatory network formed by the interaction of genes in cells. It is the main mechanism of controlling gene expression in organisms. The construction of gene regulatory networks is one of the important means to understand the nature of life activities. Therefore, the construction of gene regulatory networks using high-throughput experimental data, especially microarray data, has become a hot research topic in the field of system biology. However, most of the existing methods of constructing gene regulation network based on microarray have some problems, such as the direction of regulation can not be determined or the computational complexity is too high. In this paper, a method of constructing gene regulatory network is proposed, which combines the existing correlation test method and ordinary differential equation modeling method. Firstly, the Pearson correlation coefficient between genes is calculated by perturbing experimental data, and then an initial gene regulation network is constructed by Z-score sequencing method. On this basis, the initial control network is optimized by using time series data and ordinary differential equation modeling method. After the ordinary differential equation model is established, the problem of gene network derivation is transformed into a model parameter estimation problem. In this paper, a Tabu search based particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters. In order to reduce the computational complexity, the time series expression spectrum data are first fitted by curve fitting method, and the differential of each time point is estimated. In this way, the parameter estimation problem of differential equations is transformed into a pseudo-multivariate linear regression problem, and the computational time is greatly reduced. Finally, we use the standard test set and the real microarray data to verify the proposed method. The results show that the algorithm proposed in this paper is more sensitive, specific and accurate than the existing methods in the construction of gene regulation network. At the same time, the computational speed of the proposed algorithm is faster than that of the existing methods.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q811.4

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