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(gè)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(duì)應(yīng)的表型對(duì)癥下藥,或者避免遺傳疾病的產(chǎn)生。隨著越來(lái)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(duì)應(yīng)關(guān)系將從生物學(xué)實(shí)驗(yàn)階段步入新的計(jì)算機(jī)實(shí)驗(yàn)階段。 本文受到原有的微RNA-疾病對(duì)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(duì)未知的微RNA-疾病關(guān)聯(lián)關(guān)系進(jìn)行打分排序,針對(duì)排序結(jié)果進(jìn)行預(yù)測(cè)。主要的研究?jī)?nèi)容和成果如下： 利用已發(fā)表文獻(xiàn)挖掘經(jīng)過(guò)生物學(xué)實(shí)驗(yàn)驗(yàn)證的微RNA-疾病的對(duì)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(jì)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(diǎn)游走至下一節(jié)點(diǎn)的概率是相等的,并沒(méi)有結(jié)合網(wǎng)絡(luò)本身的特性來(lái)進(jìn)行判斷,針對(duì)這一問(wèn)題,本文提出了結(jié)合疾病網(wǎng)絡(luò)的實(shí)際情況,構(gòu)建基于節(jié)點(diǎn)重要性的疾病重要性矩陣,將節(jié)點(diǎn)重要性作為權(quán)重應(yīng)用到轉(zhuǎn)移概率矩陣中。同時(shí),結(jié)合原始數(shù)據(jù)中的對(duì)應(yīng)關(guān)系,本文為網(wǎng)絡(luò)中已對(duì)應(yīng)關(guān)系的疾病節(jié)點(diǎn)設(shè)置了一個(gè)評(píng)估參數(shù),用來(lái)提升對(duì)應(yīng)節(jié)點(diǎn)的轉(zhuǎn)移概率,從而加大排序的正確性。 實(shí)驗(yàn)結(jié)果驗(yàn)證了算法的可行性。實(shí)驗(yàn)結(jié)果得出本文365個(gè)微RNA關(guān)聯(lián)的疾病排序,通過(guò)對(duì)每個(gè)RNA樣本排名前20位疾病進(jìn)行驗(yàn)證,證明了本文提出算法的準(zhǔn)確性。同時(shí)與其他算法進(jìn)行對(duì)比,驗(yàn)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(gè)百分點(diǎn)。算法的結(jié)果可作為生物學(xué)實(shí)驗(yàn)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(shí)驗(yàn)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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R3416;TP301.6

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本文編號(hào)：1747348