本文選題：miRNA　切入點(diǎn)：疾病　出處：《哈爾濱工業(yè)大學(xué)》2008年碩士論文　論文類型：學(xué)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(xué)數(shù)據(jù)正迅速增多。由此,通過尋找生物學(xué)數(shù)據(jù)之間的聯(lián)系,生物信息學(xué)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(bǔ)傳統(tǒng)實(shí)驗(yàn)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(biāo)的尋找、藥物的篩選等過程提供重要的指導(dǎo)作用。 本文主要研究基于蛋白質(zhì)互作網(wǎng)絡(luò)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(xué)數(shù)據(jù)的特征,確定了一套有效的生物學(xué)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(xué)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(luò),并對該網(wǎng)絡(luò)作了三類拓?fù)鋮?shù)(度,簇系數(shù),最短路徑條數(shù))的統(tǒng)計(jì)分析。接著,利用miRNA的靶點(diǎn)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(luò)之間的聯(lián)系。最后,把貝葉斯后驗(yàn)概率的數(shù)學(xué)模型應(yīng)用于疾病相關(guān)的蛋白質(zhì)互作網(wǎng)絡(luò)的拓?fù)鋮?shù)分析上,成功實(shí)現(xiàn)了預(yù)測疾病相關(guān)miRNA的目的。 此外,本文在算法設(shè)計(jì)的基礎(chǔ)上,完成了乳腺癌相關(guān)miRNA的預(yù)測工作,并對算法的有效性做了多次四倍交叉驗(yàn)證。結(jié)果顯示,在正確指數(shù)為0.32的情況下,算法預(yù)測準(zhǔn)確率能夠達(dá)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(luò)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(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2008
【分類號】：R341

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 駱建新,鄭崛村,馬用信,張思仲;人類基因組計(jì)劃與后基因組時代[J];中國生物工程雜志;2003年11期

2 王芳;余佳;張俊武;;小RNA(MicroRNA)研究方法[J];中國生物化學(xué)與分子生物學(xué)報(bào);2006年10期

3 周冬根,羅玉萍,李思光;MicroRNA的結(jié)構(gòu)、生物合成及功能[J];生物技術(shù)通報(bào);2005年05期

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本文編號：1640733