本文選題：miRNA　切入點：疾病　出處：《哈爾濱工業(yè)大學》2008年碩士論文　論文類型：學位論文

【摘要】：在人類胚胎發(fā)育和疾病發(fā)生等過程中,miRNA扮演著重要的調(diào)控角色。而隨著miRNA研究的深入,有關(guān)miRNA的生物學數(shù)據(jù)正迅速增多。由此,通過尋找生物學數(shù)據(jù)之間的聯(lián)系,生物信息學研究者開始考慮從純數(shù)據(jù)角度來研究miRNA與疾病的關(guān)系,以填補傳統(tǒng)實驗方法研究的缺陷。疾病相關(guān)miRNA挖掘的研究將對疾病的診斷治療、藥物靶標的尋找、藥物的篩選等過程提供重要的指導作用。 本文主要研究基于蛋白質(zhì)互作網(wǎng)絡的疾病相關(guān)miRNA的挖掘方法。首先,分析了miRNA、疾病相關(guān)的生物學數(shù)據(jù)的特征,確定了一套有效的生物學數(shù)據(jù)作為本文研究的基礎(chǔ)。然后,利用選擇好的生物學數(shù)據(jù),定義并構(gòu)建了疾病相關(guān)的蛋白質(zhì)互作網(wǎng)絡,并對該網(wǎng)絡作了三類拓撲參數(shù)(度,簇系數(shù),最短路徑條數(shù))的統(tǒng)計分析。接著,利用miRNA的靶點數(shù)據(jù)特征,建立了miRNA與疾病相關(guān)的蛋白質(zhì)互作網(wǎng)絡之間的聯(lián)系。最后,把貝葉斯后驗概率的數(shù)學模型應用于疾病相關(guān)的蛋白質(zhì)互作網(wǎng)絡的拓撲參數(shù)分析上,成功實現(xiàn)了預測疾病相關(guān)miRNA的目的。 此外,本文在算法設(shè)計的基礎(chǔ)上,完成了乳腺癌相關(guān)miRNA的預測工作,并對算法的有效性做了多次四倍交叉驗證。結(jié)果顯示,在正確指數(shù)為0.32的情況下,算法預測準確率能夠達到70%左右,從而有效地論證了本文基于蛋白質(zhì)互作網(wǎng)絡的疾病相關(guān)miRNA挖掘算法的正確性。
[Abstract]:MiRNAs play an important role in the regulation of human embryonic development and disease. With the development of miRNA, the biological data about miRNA are increasing rapidly. Bioinformatics researchers begin to consider studying the relationship between miRNA and disease from the perspective of pure data to fill the defects of traditional experimental methods. The research of disease-related miRNA mining will be used to diagnose and treat diseases and find drug targets. Drug screening and other processes provide important guidance. In this paper, we mainly study the method of miRNA mining based on protein interaction network. Firstly, we analyze the characteristics of miRNAs and disease-related biological data, and determine a set of effective biological data as the basis of this study. Using the selected biological data, the disease related protein interaction network is defined and constructed, and three kinds of topological parameters (degree, cluster coefficient, shortest path number) are analyzed. Based on the target data of miRNA, the relationship between miRNA and disease-related protein interaction networks is established. Finally, the mathematical model of Bayesian posteriori probability is applied to the topological parameter analysis of disease-related protein interaction networks. The goal of predicting disease related miRNA has been successfully achieved. In addition, on the basis of the algorithm design, the prediction work of breast cancer related miRNA is completed, and the validity of the algorithm is verified four times. The results show that the correct index is 0.32. The prediction accuracy of the algorithm can reach about 70%, which effectively proves the correctness of the disease related miRNA mining algorithm based on protein interaction network in this paper.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2008
【分類號】：R341

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本文編號：1640733