發(fā)布時(shí)間：2018-06-02 21:43

  本文選題：生物信息學(xué) + 生物網(wǎng)絡(luò)�。� 參考：《哈爾濱工業(yè)大學(xué)》2010年博士論文

【摘要】： 非編碼RiboNucleic Acid (RNA)是生物信息學(xué)領(lǐng)域當(dāng)前的研究熱點(diǎn)。步入21世紀(jì)以來,非編碼RNA的相關(guān)研究連續(xù)獲得Science評(píng)選的年度十大科學(xué)突破,并在2006年獲得了諾貝爾生理學(xué)或醫(yī)學(xué)獎(jiǎng)。MicroRNA是一類重要的非編碼RNA,它的異常能導(dǎo)致人類疾病的發(fā)生、發(fā)展。通過生物實(shí)驗(yàn)的方法能夠挖掘疾病microRNA,但是實(shí)驗(yàn)方法代價(jià)高、周期長。本文從生物信息學(xué)的角度提出四種疾病microRNA的挖掘方法,挖掘出潛在的導(dǎo)致該疾病發(fā)生的microRNA,從而為生物學(xué)、醫(yī)學(xué)研究者有針對(duì)性地進(jìn)行microRNA生物實(shí)驗(yàn)提供一定指導(dǎo),進(jìn)而為藥物開發(fā)、臨床診斷治療提供一定的依據(jù)。本文的主要內(nèi)容包括: (1)挖掘及分析已知的microRNA與疾病關(guān)系 自2002年以來,越來越多的研究證明microRNA失調(diào)有助于疾病的發(fā)生發(fā)展,然而這些已知的疾病與microRNA關(guān)聯(lián)關(guān)系分散在已發(fā)表的文獻(xiàn)當(dāng)中,目前還沒有研究機(jī)構(gòu)建立在線共享的數(shù)據(jù)庫,收集、存儲(chǔ)、管理這些數(shù)據(jù);科研人員不易獲取這些已知的microRNA與疾病的關(guān)聯(lián)信息。因此我們先從文獻(xiàn)中挖掘已知的疾病microRNA ,構(gòu)建了全球首個(gè)microRNA與疾病關(guān)系數(shù)據(jù)庫(miR2Disease),并對(duì)數(shù)據(jù)進(jìn)行管理。對(duì)miR2Disease中的數(shù)據(jù)進(jìn)行分析,發(fā)現(xiàn)多種疾病往往共享一些致病microRNA,擁有部分相似的發(fā)病機(jī)制;此外,總結(jié)出疾病microRNA失調(diào)的三種機(jī)制:首先,疾病microRNA常位于與疾病有關(guān)的基因座內(nèi),例如雜合子缺失的微小區(qū)域、微小擴(kuò)增區(qū)域或斷裂位點(diǎn)等脆性位點(diǎn)區(qū)域;其次,疾病microRNA失調(diào)是由異常的表觀遺傳信息改變所致;例如DNA異常甲基化、組蛋白異常修飾等等;最后,疾病microRNA失調(diào)是由參與microRNA生物合成的酶的功能異常所致。 (2)提出基于布爾網(wǎng)絡(luò)的疾病microRNA挖掘技術(shù) 生物網(wǎng)絡(luò)在挖掘編碼蛋白的疾病基因方面發(fā)揮了重要作用,然后在疾病microRNA挖掘領(lǐng)域,至今還未提出基于生物網(wǎng)絡(luò)的疾病microRNA挖掘方法。因此本文提出了構(gòu)造布爾型的功能相關(guān)microRNA網(wǎng)絡(luò)的算法,以網(wǎng)絡(luò)的形式來研究microRNA。通過對(duì)網(wǎng)絡(luò)的分析,我們發(fā)現(xiàn)布爾型microRNA網(wǎng)絡(luò)像其他生物網(wǎng)絡(luò)一樣,網(wǎng)絡(luò)的度服從冪分布,網(wǎng)絡(luò)具有層次模塊性等特點(diǎn)。我們進(jìn)一步構(gòu)建了phenome-microRNAome網(wǎng)絡(luò),在此網(wǎng)絡(luò)上,對(duì)已知的疾病microRNA進(jìn)行分析,發(fā)現(xiàn)“功能相關(guān)的microRNA失調(diào)傾向于導(dǎo)致表型相同或相似的疾病”這一規(guī)律。以此為理論基礎(chǔ),提出了基于布爾型生物網(wǎng)絡(luò)的疾病microRNA挖掘算法,并驗(yàn)證了算法的有效性。 (3)提出基于權(quán)重型網(wǎng)絡(luò)的疾病microRNA挖掘技術(shù) 基于布爾網(wǎng)絡(luò)的疾病microRNA挖掘技術(shù)在構(gòu)造布爾型microRNA網(wǎng)絡(luò)只需根據(jù)靶基因重疊的顯著性來確定二個(gè)microRNA之間的關(guān)聯(lián)關(guān)系。當(dāng)知道m(xù)icroRNA對(duì)靶基因的抑制強(qiáng)度信息時(shí),可以利用該信息構(gòu)建權(quán)重型網(wǎng)絡(luò)。因此,我們提出了基于權(quán)重型網(wǎng)絡(luò)的疾病microRNA挖掘方法,取得了很好的性能。 (4)提出基于支持向量機(jī)的疾病microRNA挖掘方法 為了直接從數(shù)據(jù)出發(fā)挖掘疾病microRNA,我們把疾病microRNA的挖掘問題轉(zhuǎn)化為一個(gè)分類問題,提出了基于支持向量機(jī)的疾病microRNA挖掘方法,把數(shù)據(jù)挖掘、機(jī)器學(xué)習(xí)的思想引入到疾病microRNA的挖掘中并交叉驗(yàn)證了方法的有效性。 (5)提出基于基因組數(shù)據(jù)融合的疾病microRNA挖掘技術(shù) 統(tǒng)計(jì)數(shù)據(jù)表明近三年獲得的生物醫(yī)學(xué)數(shù)據(jù)超過過去四萬年的總和,數(shù)據(jù)呈爆炸增長,面對(duì)浩瀚的生物學(xué)數(shù)據(jù)海洋,如何把這海量的數(shù)據(jù)轉(zhuǎn)化為有意義的醫(yī)學(xué)診斷和治療信息并惠及人類自身的健康是21世紀(jì)生物醫(yī)學(xué)信息學(xué)面臨的嚴(yán)峻挑戰(zhàn)。本章整合了多種生物數(shù)據(jù)資源,構(gòu)建了全人類基因組范圍的基因功能相關(guān)網(wǎng)絡(luò),在此網(wǎng)絡(luò)基礎(chǔ)上,提出了利用microRNA的靶基因與已知的感興趣疾病的致病基因之間在網(wǎng)絡(luò)上的功能關(guān)系來挖掘新的潛在的疾病microRNA的算法,將算法應(yīng)用到結(jié)腸癌上驗(yàn)證了算法的有效性。
[Abstract]:Non coded RiboNucleic Acid (RNA) is the current research hotspot in the field of bioinformatics. Since entering twenty-first Century, the related research of non coded RNA has continuously obtained the ten major scientific breakthroughs of the annual Science selection, and in 2006 the Nobel prize for physiology or medicine.MicroRNA is an important non coded RNA, and its abnormality can lead to human beings. The occurrence and development of the disease can be developed through biological experiments, but the experimental method is able to excavate the disease microRNA, but the experimental method is expensive and the cycle is long. From the perspective of bioinformatics, this paper proposes a mining method of four diseases, microRNA, to excavate the potential microRNA that causes the disease to occur, from the biological, and the medical researchers are targeted to the mic. RoRNA biological experiments provide some guidance for further development of drugs and clinical diagnosis and treatment.
(1) mining and analyzing the known relationship between microRNA and disease
Since 2002, more and more studies have shown that microRNA disorders contribute to the development of the disease. However, the relationship between these known diseases and microRNA is scattered in the published literature. There is no research organization to establish online shared databases, collection, storage, and management of these data; researchers are not easy to obtain these already. We know the association information between microRNA and disease. So we first excavated the known disease microRNA from the literature, constructed the world's first microRNA and disease relational database (miR2Disease), and managed the data. We analyzed the data in miR2Disease and found that a variety of diseases often share some of the pathogenic microRNA and have some similarities. In addition, three mechanisms of disease microRNA disorders are summarized: first, the disease microRNA is often located in the loci related to the disease, such as the tiny region of the heterozygote deletion, the small amplification area or the fracture site and other fragile sites; secondly, the disorder of the disease microRNA is caused by abnormal epigenetic information; Abnormal methylation of DNA, histone modification, and so on. Finally, the disorder of microRNA is caused by dysfunction of enzymes involved in microRNA biosynthesis.
(2) propose microRNA mining technology based on Boolean network.
Biological network plays an important role in mining the disease genes encoding protein, and then in the field of disease microRNA mining, so far, the disease microRNA mining method based on biological network has not been proposed. Therefore, this paper proposes a algorithm for constructing a Boolean functional related microRNA network, which is used in the form of network to study microRNA. through the form of network. Network analysis, we find that Boolean microRNA networks like other biological networks, the degree of the network is subordinate to power distribution, and the network has the characteristics of hierarchical modularity. We further construct the phenome-microRNAome network, on which the known disease microRNA is analyzed, and that "functional related microRNA disorders tend to lead to guidance." On the basis of the theory, a disease microRNA mining algorithm based on Boolean biological network is proposed, and the effectiveness of the algorithm is verified.
(3) put forward the microRNA mining technology based on weight heavy network.
The disease microRNA mining technology based on Boolean networks can determine the association between two microRNA based on the significance of target gene overlap in constructing Boolean microRNA networks. When the microRNA is known to suppress the target gene, we can use the information to build a heavy network. Therefore, we propose a weight based heavy weight network. Network disease microRNA mining method has achieved very good performance.
(4) propose a method of disease microRNA mining based on support vector machine.
In order to mine disease microRNA directly from data, we transform the problem of disease microRNA into a classification problem, and propose a disease microRNA mining method based on support vector machine, which introduces the idea of data mining, machine learning to the mining of disease microRNA and cross verifies the effectiveness of the method.
(5) propose microRNA mining technology based on genome data fusion.
Statistics show that the biomedical data obtained in the last three years are more than the sum of the past forty thousand years, and the data are increasing. In the face of the vast ocean of biological data, how to translate this mass of data into meaningful medical diagnosis and treatment information and benefit human health is a severe challenge for biomedical informatics in twenty-first Century. In this chapter, a variety of biological data resources are integrated, and the gene function related network of the whole human genome is constructed. On the basis of this network, a new algorithm for mining potential disease microRNA using the functional relationship between the target gene of microRNA and the pathogenetic genes of the interested diseases is proposed, and the algorithm should be used. The effectiveness of the algorithm was verified with colon cancer.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2010
【分類號(hào)】：R341

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 刁麗紅;蛋白質(zhì)相互作用網(wǎng)絡(luò)在癌癥研究中應(yīng)用以及金屬抗癌配合物的合成[D];廣西民族大學(xué);2012年

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本文編號(hào)：1970302