本文選題：時(shí)序基因表達(dá)數(shù)據(jù)　切入點(diǎn)：基因調(diào)控網(wǎng)絡(luò)　出處：《西安電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：基因芯片技術(shù)的應(yīng)用使得快速獲得大量基因表達(dá)數(shù)據(jù)成為可能,進(jìn)而為生物信息學(xué)研究提供了必需的數(shù)據(jù)庫(kù),極大地推動(dòng)了基因數(shù)據(jù)的研究。細(xì)胞的生命活動(dòng)與細(xì)胞內(nèi)所有基因的表達(dá)水平有關(guān),一個(gè)基因的表達(dá)并不是孤立的,它的表達(dá)水平不僅受到其它基因的影響,同時(shí)還可能影響其它基因的表達(dá)水平,這些基因間的相互調(diào)控關(guān)系構(gòu)成了基因調(diào)控網(wǎng)絡(luò)。同樣地,基因調(diào)控網(wǎng)絡(luò)是眾多基因復(fù)雜調(diào)控的體現(xiàn)。對(duì)基因調(diào)控網(wǎng)絡(luò)進(jìn)行研究能夠幫助我們了解基因與基因之間或者基因及其產(chǎn)物之間的相互關(guān)系;對(duì)疾病基因調(diào)控網(wǎng)絡(luò)的分析,可以指導(dǎo)藥物研發(fā)和臨床合理用藥,從而為復(fù)雜疾病的診斷與治療提供有價(jià)值的信息。所以重構(gòu)和分析基因調(diào)控網(wǎng)絡(luò)是十分必要的。傳統(tǒng)的調(diào)控網(wǎng)絡(luò)構(gòu)建方法大多是借助各種網(wǎng)絡(luò)模型,如布爾網(wǎng)絡(luò)模型和貝葉斯網(wǎng)絡(luò)模型等等,但這些模型忽略了基因表達(dá)數(shù)據(jù)的時(shí)序性特征。本文致力于研究利用時(shí)序基因表達(dá)數(shù)據(jù)重構(gòu)基因調(diào)控網(wǎng)絡(luò)的算法,并提出基于Granger因果關(guān)系的基因調(diào)控網(wǎng)絡(luò)構(gòu)建新算法,以展現(xiàn)基因間的因果調(diào)控關(guān)系。算法的提出基于以下基礎(chǔ):(1)時(shí)序基因表達(dá)數(shù)據(jù)作為一種特殊的時(shí)序數(shù)據(jù),時(shí)序數(shù)據(jù)的分析處理方法同樣適用于基因表達(dá)數(shù)據(jù)的處理。(2)基因的表達(dá)水平是自身表達(dá)與基因間相互調(diào)控的體現(xiàn),解讀基因的表達(dá)水平可以分析基因間的調(diào)控關(guān)系。同時(shí),Granger因果關(guān)系是分析時(shí)序變量因果性的有效工具,可以應(yīng)用于基因表達(dá)水平的分析。本文主要涉及自回歸模型和Granger因果關(guān)系等理論知識(shí),我們將由多基因組成的時(shí)序基因數(shù)據(jù)出發(fā),利用自回歸模型抽取基因前后時(shí)間表達(dá)值互相依賴的動(dòng)態(tài)特性,并利用譜分析的知識(shí)將時(shí)序數(shù)據(jù)映射到頻域達(dá)到降低維度和消除時(shí)間對(duì)因果性的影響,通過計(jì)算基因間兩兩Granger因果調(diào)控關(guān)系,建立包含所有基因在內(nèi)的基于Granger因果關(guān)系的基因調(diào)控網(wǎng)絡(luò)。通過在大腸桿菌和酵母菌模擬數(shù)據(jù)上實(shí)驗(yàn),驗(yàn)證了自回歸模型適用于時(shí)序基因表達(dá)數(shù)據(jù)。在釀酒酵母菌真實(shí)基因表達(dá)數(shù)據(jù)上實(shí)驗(yàn),與NIR算法比較基于Granger因果關(guān)系的構(gòu)建算法具有較高的識(shí)別率。
[Abstract]:The application of gene chip technology makes it possible to obtain a large number of gene expression data quickly, which provides the necessary database for bioinformatics research. The activity of cell life is related to the level of expression of all genes in the cell. The expression of one gene is not isolated, and its expression level is not only affected by other genes. It may also affect the level of expression of other genes, which form a genetic regulatory network. Gene regulation network is the embodiment of many gene complex regulation. The study of gene regulation network can help us understand the relationship between gene and gene or gene and its products, and analyze disease gene regulation network. Can guide drug research and development and clinical rational use, Thus providing valuable information for the diagnosis and treatment of complex diseases. So it is very necessary to reconstruct and analyze gene regulatory networks. Such as Boolean network model and Bayesian network model, but these models ignore the temporal characteristics of gene expression data. A new algorithm based on Granger causality is proposed to show the relationship between genes. The algorithm is based on the following basic: 1) temporal gene expression data as a special temporal data. The analysis and processing method of time series data is also suitable for the processing of gene expression data. The expression level of gene is the embodiment of self-expression and interregulation between genes. The level of gene expression can be used to analyze the regulatory relationship between genes, and Granger causality is an effective tool to analyze the causality of temporal variables. It can be applied to the analysis of gene expression level. This paper mainly involves the autoregressive model and Granger causality and other theoretical knowledge, we will be composed of multiple gene sequence gene data, The autoregressive model is used to extract the dynamic characteristics of interdependency between time expression values before and after gene expression, and the time series data are mapped to frequency domain to reduce the dimension and eliminate the effect of time on causality by using the knowledge of spectral analysis. By calculating the Granger causality control relationship between genes, a Granger cause-and-effect regulatory network based on Granger causality was established, which includes all genes. The experiments were carried out on the simulated data of Escherichia coli and yeast. It is verified that the autoregressive model is suitable for the sequential gene expression data. Compared with the NIR algorithm, the construction algorithm based on Granger causality has a higher recognition rate in the experiments on the real gene expression data of Saccharomyces cerevisiae.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：Q811.4;O212.1

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