本文選題：生物信息學(xué) + 微RNA　； 參考：《中南大學(xué)》2014年碩士論文

【摘要】：在生物信息學(xué)領(lǐng)域,疾病與基因之間的關(guān)聯(lián)關(guān)系是一個極其重要的研究方向。是研究人類遺傳疾病的重要手段,掌握了復(fù)雜疾病的治病機(jī)理,就可以根據(jù)致病基因所對應(yīng)的表型對癥下藥,或者避免遺傳疾病的產(chǎn)生。隨著越來越多的基因被發(fā)現(xiàn),數(shù)據(jù)量的飛速提升,基因與疾病的對應(yīng)關(guān)系將從生物學(xué)實驗階段步入新的計算機(jī)實驗階段。 本文受到原有的微RNA-疾病對應(yīng)關(guān)系算法研究的啟發(fā),提出了利用改進(jìn)的隨機(jī)游走算法在構(gòu)建的疾病相似性網(wǎng)絡(luò)上進(jìn)行隨機(jī)游走,對未知的微RNA-疾病關(guān)聯(lián)關(guān)系進(jìn)行打分排序,針對排序結(jié)果進(jìn)行預(yù)測。主要的研究內(nèi)容和成果如下： 利用已發(fā)表文獻(xiàn)挖掘經(jīng)過生物學(xué)實驗驗證的微RNA-疾病的對應(yīng)關(guān)系作為原始數(shù)據(jù)。利用基于Mesh概念的相似性算法計算相關(guān)疾病的相似性,用以構(gòu)建疾病相似性網(wǎng)絡(luò)。在傳統(tǒng)的隨機(jī)游走算法中默認(rèn)初始節(jié)點游走至下一節(jié)點的概率是相等的,并沒有結(jié)合網(wǎng)絡(luò)本身的特性來進(jìn)行判斷,針對這一問題,本文提出了結(jié)合疾病網(wǎng)絡(luò)的實際情況,構(gòu)建基于節(jié)點重要性的疾病重要性矩陣,將節(jié)點重要性作為權(quán)重應(yīng)用到轉(zhuǎn)移概率矩陣中。同時,結(jié)合原始數(shù)據(jù)中的對應(yīng)關(guān)系,本文為網(wǎng)絡(luò)中已對應(yīng)關(guān)系的疾病節(jié)點設(shè)置了一個評估參數(shù),用來提升對應(yīng)節(jié)點的轉(zhuǎn)移概率,從而加大排序的正確性。 實驗結(jié)果驗證了算法的可行性。實驗結(jié)果得出本文365個微RNA關(guān)聯(lián)的疾病排序,通過對每個RNA樣本排名前20位疾病進(jìn)行驗證,證明了本文提出算法的準(zhǔn)確性。同時與其他算法進(jìn)行對比,驗證了此算法的高效性,此外該算法的算法的AUC值達(dá)到了73.5%,相較于傳統(tǒng)算法高出了一個百分點。算法的結(jié)果可作為生物學(xué)實驗的重要參考依據(jù)進(jìn)一步的應(yīng)用于基因-疾病關(guān)聯(lián)關(guān)系的實驗之中。
[Abstract]:In the field of bioinformatics, the relationship between disease and gene is a very important research direction.It is an important means to study human genetic diseases. If we master the therapeutic mechanism of complex diseases, we can use the corresponding phenotypes of pathogenic genes to prescribe medicine, or to avoid the occurrence of genetic diseases.With the discovery of more and more genes and the rapid improvement of data volume, the corresponding relationship between gene and disease will move from the stage of biological experiment to a new stage of computer experiment.Inspired by the original algorithm of microRNA-disease correspondence, this paper proposes to use the improved random walk algorithm on the disease similarity network to rank the unknown micro-RNA-disease relationship.The results of sorting are predicted.The main research contents and results are as follows:The published literatures were used to mine the corresponding relationship between microRNAs and diseases verified by biological experiments as raw data.The similarity algorithm based on Mesh concept is used to calculate the similarity of related diseases to construct the disease similarity network.In the traditional random walk algorithm, the probability of the initial node walking to the next node is equal by default, and it is not judged by the characteristics of the network itself. In view of this problem, this paper proposes to combine the actual situation of the disease network.The disease importance matrix based on node importance is constructed, and the node importance is applied to the transfer probability matrix.At the same time, combined with the correspondence in the original data, this paper sets an evaluation parameter for the corresponding disease nodes in the network, which is used to enhance the transfer probability of the corresponding nodes, thus increasing the accuracy of sorting.The experimental results verify the feasibility of the algorithm.The experimental results show that 365 micro associated diseases are ranked in this paper, and the accuracy of the proposed algorithm is proved by verifying the top 20 diseases in each RNA sample.At the same time, compared with other algorithms, the efficiency of the algorithm is verified. In addition, the AUC value of the algorithm reaches 73.5%, which is one percentage point higher than the traditional algorithm.The results of the algorithm can be used as an important reference for biological experiments.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R3416;TP301.6

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本文編號：1747348