發(fā)布時(shí)間：2018-01-06 12:37

  本文關(guān)鍵詞：利用生物學(xué)網(wǎng)絡(luò)研究疾病的分子機(jī)制和預(yù)后　出處：《中國人民解放軍軍事醫(yī)學(xué)科學(xué)院》2011年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 疾病相關(guān)基因 基因轉(zhuǎn)錄調(diào)控網(wǎng)絡(luò) 生物學(xué)通路 蛋白質(zhì)相互作用網(wǎng)絡(luò) 疾病預(yù)后

【摘要】：疾病時(shí)刻威脅著人類的健康和生活,嚴(yán)重情況下甚至可以導(dǎo)致死亡。由于技術(shù)水平的限制,多年以來,小規(guī)模、低通量的疾病遺傳學(xué)研究往往停留在實(shí)驗(yàn)室水平上,對(duì)疾病的臨床控制作用有限。近年來,隨著基因組學(xué)和蛋白質(zhì)組學(xué)的發(fā)展,眾多研究機(jī)構(gòu)利用組學(xué)實(shí)驗(yàn)手段產(chǎn)生了海量的與疾病有關(guān)的實(shí)驗(yàn)數(shù)據(jù),用于研究疾病的發(fā)生、發(fā)展過程以及尋找可能的治療方法。通過生物信息學(xué)手段對(duì)海量數(shù)據(jù)的大規(guī)模挖掘,人們發(fā)現(xiàn)了大量基因、蛋白質(zhì)以及生物學(xué)通路與疾病表型相關(guān)聯(lián),這些研究成果為疾病機(jī)制的研究和可能的臨床診斷奠定了基礎(chǔ)。 盡管疾病的組學(xué)研究已經(jīng)取得了長足發(fā)展,但是仍然存在一些亟待解決的問題:首先,某些研究工作往往圍繞著特定疾病的病理過程進(jìn)行設(shè)計(jì),很難推廣到其他疾病研究中去;其次,一些基于大規(guī)模基因芯片表達(dá)數(shù)據(jù)的研究,鑒定出了大量差異表達(dá)的基因或者蛋白質(zhì),但對(duì)于如何解讀這些結(jié)果,尤其是如何揭示這些基因或者蛋白質(zhì)聯(lián)合作用對(duì)疾病發(fā)生發(fā)展的影響卻不甚明了;再次,很多研究往往使用單一生物學(xué)實(shí)驗(yàn)數(shù)據(jù),分析結(jié)果的假陽性高;另外,缺少通用的跨實(shí)驗(yàn)平臺(tái)的組學(xué)數(shù)據(jù)綜合分析軟件。 為解決以上問題,本文從多種組學(xué)數(shù)據(jù)和先驗(yàn)生物學(xué)知識(shí)出發(fā),設(shè)計(jì)和建立了一系列數(shù)學(xué)模型和分析策略,有效地鑒定出了一系列與疾病相關(guān)的通路和蛋白質(zhì)相互作用子網(wǎng),并用鑒定出的蛋白質(zhì)子網(wǎng)成功地預(yù)測(cè)了乳腺癌的預(yù)后。本文建立的研究方法和體系可應(yīng)用于不同疾病的機(jī)制和診斷研究,具體內(nèi)容如下: 首先,為了預(yù)測(cè)疾病條件下基因間的調(diào)控關(guān)系,本文基于基因芯片數(shù)據(jù),發(fā)展了一種整合主成分分析、皮爾森相關(guān)系數(shù)和支持向量機(jī)分類器的預(yù)測(cè)策略。目前現(xiàn)有的很多調(diào)控關(guān)系預(yù)測(cè)方法,都是直接基于原始數(shù)據(jù)進(jìn)行分析預(yù)測(cè)的,這些方法忽略了芯片數(shù)據(jù)的噪聲影響,以及基因之間的相互作用關(guān)系。利用數(shù)據(jù)降維算法能抽取基因芯片數(shù)據(jù)的關(guān)鍵信息,降低噪聲影響;而結(jié)合基因表達(dá)相關(guān)性參數(shù)——皮爾森相關(guān)系數(shù)(PCC),能夠同時(shí)考慮基因間的相互關(guān)系。我們利用數(shù)據(jù)降維算法——主成分分析法(PCA)抽取基因表達(dá)特征,進(jìn)而利用這些特征和基因表達(dá)水平之間的皮爾森相關(guān)系數(shù)建立了用于衡量基因間調(diào)控關(guān)系的新參數(shù)FAB,并將其輸入到支持向量機(jī)分類器(SVM)里面,預(yù)測(cè)基因間的調(diào)控關(guān)系。預(yù)測(cè)結(jié)果顯示,選擇合適的數(shù)據(jù)降維算法和合適的特征向量定義的調(diào)控參數(shù),能以較高的準(zhǔn)確度、特異度和靈敏度預(yù)測(cè)基因間的調(diào)控關(guān)系,這項(xiàng)工作為研究疾病條件下基因間的調(diào)控關(guān)系奠定了基礎(chǔ)。 其次,為了研究疾病的發(fā)生發(fā)展機(jī)制,本文提出了一套疾病相關(guān)通路和重要基因的鑒定策略,并成功將其應(yīng)用到了II型糖尿病患者和吸煙影響的人群數(shù)據(jù)集上。通過整合疾病基因芯片表達(dá)數(shù)據(jù)集和已有生物學(xué)通路數(shù)據(jù)庫(KEGG通路數(shù)據(jù)庫和BioCarta通路數(shù)據(jù)庫),首次引入非負(fù)矩陣分解分析策略(NMFAS)分析疾病人群和正常人群的通路活性水平的差異表達(dá)情況,并解決了該算法解不唯一性問題,鑒定出了疾病人群機(jī)體內(nèi)活性顯著差異的生物學(xué)通路,并通過分析通路成員基因?qū)ν坊钚缘呢暙I(xiàn)值,鑒定出與疾病表型潛在相關(guān)的重要基因,從而為研究疾病的發(fā)生發(fā)展過程提供了重要線索。 最后,本文給出了一種基于蛋白質(zhì)相互作用網(wǎng)絡(luò)的疾病診斷和預(yù)后預(yù)測(cè)策略。以乳腺癌轉(zhuǎn)移數(shù)據(jù)為研究對(duì)象,從已知的乳腺癌相關(guān)基因出發(fā),利用隨機(jī)行走算法(Random Walk)在人類蛋白質(zhì)相互作用網(wǎng)絡(luò)中尋找潛在的乳腺癌相關(guān)子網(wǎng),并進(jìn)而基于這些子網(wǎng)的基因累積表達(dá)信息,利用支持向量機(jī)(SVM)分類器預(yù)測(cè)乳腺癌的轉(zhuǎn)移。通過對(duì)標(biāo)準(zhǔn)數(shù)據(jù)集的分析,該算法能夠有效的找到疾病表型相關(guān)基因和乳腺癌相關(guān)蛋白質(zhì)相互作用子網(wǎng),并且在預(yù)測(cè)乳腺癌轉(zhuǎn)移時(shí),該策略在預(yù)測(cè)正確率、敏感度和特異度方面取得了理想的結(jié)果。 總之,本文從基因、基因調(diào)控關(guān)系、蛋白質(zhì)相互作用子網(wǎng)和生物學(xué)通路等多個(gè)方面,系統(tǒng)研究了與人類疾病的發(fā)生發(fā)展相關(guān)的分子和相互作用。通過綜合考察已有生物學(xué)知識(shí)、基因表達(dá)、基因調(diào)控、生物學(xué)通路和蛋白質(zhì)相互作用信息,利用數(shù)據(jù)降維算法、機(jī)器學(xué)習(xí)分類算法、網(wǎng)絡(luò)傳播算法等多種數(shù)據(jù)挖掘方法鑒定與疾病潛在相關(guān)的基因、蛋白質(zhì)或者蛋白質(zhì)子網(wǎng),并利用鑒定得到的疾病相關(guān)蛋白質(zhì)子網(wǎng)成功進(jìn)行了疾病診斷。本文提出的這一系列方法可以在一定程度上避免了現(xiàn)有方法的不足和限制,提高了現(xiàn)有方法的預(yù)測(cè)準(zhǔn)確度和靈敏度,從而促進(jìn)了對(duì)疾病條件下的生物分子、網(wǎng)絡(luò)乃至整個(gè)生物系統(tǒng)的理解。另外,本文方法均可從一種疾病推廣到其他疾病,具有良好的擴(kuò)展性。 本文的主要?jiǎng)?chuàng)新點(diǎn)包括:利用數(shù)據(jù)降維算法抽取基因芯片的表達(dá)特征結(jié)合基因共表達(dá)強(qiáng)度參數(shù)預(yù)測(cè)基因間的調(diào)控關(guān)系,提高了預(yù)測(cè)準(zhǔn)確率和敏感度特異度;首次引入非負(fù)矩陣分析策略鑒定疾病相關(guān)生物學(xué)通路和重要基因;利用網(wǎng)絡(luò)傳播算法分析疾病相關(guān)蛋白質(zhì)相互作用,并用于預(yù)測(cè)疾病預(yù)后,在預(yù)測(cè)敏感度、特異度方面有較大提高。幾部分研究?jī)?nèi)容互相支撐,互為補(bǔ)充,并且具有較強(qiáng)的通用性和可擴(kuò)展性,可以應(yīng)用于不同疾病的機(jī)制研究和診斷,將會(huì)為疾病標(biāo)志物和藥物靶標(biāo)的發(fā)現(xiàn)以及疾病的臨床診斷提供重要參考和幫助。
[Abstract]:The disease threatening human health and life, even in severe cases can lead to death. Due to technical limitations, over the years, small scale, low flux disease genetics studies tend to stay in the laboratory level, clinical control of disease. In recent years, with the development of genomics and proteomics many research institutions, using proteomics experiments produce vast amounts of experimental data and related diseases, to study the incidence of the disease, the development process and look for possible treatment. Through bioinformatics means large-scale mining of massive data, they found a large number of genes, proteins and biological pathways associated with disease phenotypes, lay based on these research results on mechanisms of disease and possible clinical diagnosis.
Although the research has achieved great development in disease group, but there are still some problems to be solved: firstly, some research work often revolves around the pathological process of disease specific design, it is difficult to generalize to other disease research; secondly, based on some large scale microarray gene expression data of identified genes or a large number of differentially expressed proteins, but how to interpret these results, especially how to reveal these genes or proteins combined with the effect of the development of disease is unclear; again, a lot of research often use a single biological experimental data, high false positive results; in addition, the lack of cross platform universal group the integrated data analysis software.
To solve the above problems, this paper studies data and prior biological knowledge from a variety of groups, design and set up a series of mathematical models and analysis strategy, effectively identified a series of disease-related pathways and protein interaction networks, and identified the protein network successfully to predict the prognosis of breast cancer research method and system established in this paper can be applied to the mechanism and diagnosis of different diseases, the specific contents are as follows:
First of all, in order to control the relationship between the prediction of disease conditions between genes, the microarray data based on the analysis of the development of an integrated principal component prediction strategy Pearson correlation coefficient and the support vector machine classifier. At present, many of the existing regulatory relationship prediction method is directly based on the original data analysis and forecast, these methods ignore noise in microarray data, relationship and interaction between genes. Using the data dimensionality reduction algorithm can extract the key information of gene chip data, reduce the effects of noise; and the combination of gene expression correlation parameters, Pearson correlation coefficient (PCC), can also consider the relationships between genes. We use data dimensionality reduction algorithm principal component analysis (PCA) feature extraction of gene expression, Pearson correlation coefficient and use these features and gene expression level between the established The new FAB parameters to measure the regulatory relationships between genes, and the input to the support vector machine classifier (SVM), the relationship between the regulation of gene prediction. The prediction results show that, the regulation of choosing appropriate parameters of the data dimensionality reduction algorithm and a suitable feature vector is defined, with high accuracy, specific regulation prediction and sensitivity between genes, this work has laid the foundation for the study of disease control conditions between genes.
Secondly, in order to study the mechanism of the occurrence and development of diseases, proposed a set of strategies for identification of disease related pathways and important genes, and successfully applied to the population data of patients with type II diabetes and smoking effects set. Expression data sets and the existing biological pathways through the integration of disease gene chip database (KEGG database and BioCarta pathway for the first time, access database) the introduction of non negative matrix factorization analysis strategy (NMFAS) differential expression analysis of disease population and normal population level pathway activity, and solves the problem of the algorithm is not the only solution, to identify biological pathways in the body disease activity was significantly different, and through the analysis of genes on the pathway pathway. The activity value of the contribution, identified with the disease phenotype potentially important genes that are related to occurrence and development of disease provides an important clue.
Finally, this paper presents a kind of disease diagnosis and prognosis prediction method for protein-protein interaction network. The metastasis of breast cancer data as the research object, starting from breast cancer related genes known, using random walk algorithm (Random Walk) in search of potential breast cancer associated subnet in the human protein interaction network, and then these sub network information based on gene expression of accumulation, using support vector machine (SVM) classifier to predict the metastasis of breast cancer. Through the analysis of the standard data set, the algorithm can effectively find the disease phenotype related genes and breast cancer related protein interaction sub networks, and in the prediction of breast cancer metastasis, the rate of correct strategy in the prediction, sensitivity and specificity have achieved satisfactory results.
In short, this article from the gene, gene regulation, many aspects of protein interaction sub networks and biological pathways, system research related to the development and occurrence of human disease and molecular interactions. The gene expression through a comprehensive review of existing biological knowledge, gene regulation, biological pathways and protein-protein interaction information, using data reduction the dimension of machine learning algorithm, classification algorithm, network communication algorithm and other data mining methods and identification of potential disease associated genes, proteins or protein protons, and the success of the disease diagnosis by identified disease associated protein network proton. This series of the method in this paper can to some extent avoid the shortcomings of existing methods and the limit of existing methods to improve the prediction accuracy and sensitivity, so as to promote the conditions of biological molecules, and even the whole network In addition, this method can be extended from one disease to other diseases with good expansibility.
The main innovations of this paper include: the expression characteristics of the use of data dimensionality reduction algorithm combined with gene chip gene expression intensity parameters to predict the relationship between gene regulation, to improve the prediction accuracy and sensitivity and specificity; first introduced non negative matrix analysis method to identify disease associated biological pathways and genes; using the network communication algorithm analysis disease related protein interactions, and to predict the prognosis of the disease, in predicting the sensitivity and specificity are greatly improved. Some research support each other, complement each other, and has strong versatility and scalability, can be applied to the study of mechanism and diagnosis of different diseases, will provide important reference and help the clinical the diagnosis and drug target discovery and disease as markers of the disease.

【學(xué)位授予單位】：中國人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2011
【分類號(hào)】：R341

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本文編號(hào)：1387923