本文選題：藥物副作用　切入點(diǎn)：BGLL　出處：《北京交通大學(xué)》2017年碩士論文

【摘要】：藥物副作用是病人在治療的過程中按照正常的藥物劑量進(jìn)行診斷、預(yù)防、治療某種疾病所出現(xiàn)的與治療目的無關(guān)的反應(yīng),一般會(huì)引起患者的不適和痛苦。美國的一項(xiàng)關(guān)于藥物副作用的研究表明嚴(yán)重的藥物副作用是引起人類死亡的第四大原因,每年會(huì)引起1,000,000人死亡。因此,藥物副作用逐漸成為公共健康的主要問題,其不僅是藥物研發(fā)失敗的主要原因,同時(shí)也是新藥研究與投產(chǎn)的主要阻力。目前,對于藥物副作用的研究主要集中在如下三個(gè)方面:一、利用藥物所作用的靶點(diǎn)來預(yù)測藥物副作用;二、基于藥物的化學(xué)結(jié)構(gòu)預(yù)測藥物副作用;三、利用數(shù)據(jù)挖掘技術(shù)從文獻(xiàn)庫中挖掘藥物和副作用的關(guān)系來豐富和完善現(xiàn)有的數(shù)據(jù)庫。然而,已有藥物副作用的研究僅僅考慮藥物自身特性,并未探究疾病、基因、癥狀與藥物副作用之間的關(guān)系。為此,本文主要研究內(nèi)容有以下三點(diǎn)。首先,整合來自不同數(shù)據(jù)庫中的數(shù)據(jù),形成副作用與疾病、基因、癥狀等關(guān)系數(shù)據(jù),建立基于基因的疾病網(wǎng)絡(luò),基于副作用的疾病網(wǎng)絡(luò),基于癥狀的疾病網(wǎng)絡(luò)等。為了探索副作用和疾病之間的關(guān)系,采用BGLL和BigCLAM(Cluster Affiliation Model for Big Networks,簡稱BigCLAM)兩種復(fù)雜網(wǎng)絡(luò)社團(tuán)劃分算法對基于副作用的疾病網(wǎng)絡(luò)進(jìn)行社團(tuán)劃分。兩種社團(tuán)劃分算法所得結(jié)果均具有較高的模塊度,且所得模塊具有高度的一致性,由此表明疾病和副作用之間存在相關(guān)關(guān)系。其次,從富集分析和基因一致性分析兩方面驗(yàn)證疾病和副作用之間的關(guān)系。通過對高相似度的疾病網(wǎng)絡(luò)進(jìn)行網(wǎng)絡(luò)社團(tuán)分析,得到441個(gè)模塊,采用富集分析方法對疾病模塊和疾病分類進(jìn)行分析,實(shí)驗(yàn)表明同一模塊中的疾病富集到同一疾病類別�；蛞恢滦苑治鍪抢没蚣膊【W(wǎng)絡(luò)數(shù)據(jù)和副作用疾病網(wǎng)絡(luò)數(shù)據(jù),計(jì)算兩個(gè)網(wǎng)絡(luò)在實(shí)驗(yàn)條件和隨機(jī)條件下的重疊特性。結(jié)果表明實(shí)驗(yàn)條件下網(wǎng)絡(luò)的重疊特性遠(yuǎn)遠(yuǎn)高于隨機(jī)條件,由此驗(yàn)證了疾病與副作用之間的關(guān)系。之后采用支持向量機(jī)和簡單邏輯回歸分類方法,以副作用為特征對疾病分類(文中以C01類疾病為例)。不同分類方法均可得到較高的精準(zhǔn)率、召回率和AUC(AreaUnderroc Curve,簡稱AUC),結(jié)果表明部分副作用可能是疾病本身的屬性。最后,從副作用的類別出發(fā)得到副作用主要屬于T047疾病類別和T184癥狀類別。對于T047副作用的網(wǎng)絡(luò)拓?fù)涮匦?如度分布進(jìn)行相關(guān)研究發(fā)現(xiàn)同一節(jié)點(diǎn)所關(guān)聯(lián)的疾病多數(shù)屬于同一類別,結(jié)果表明藥物治療一類疾病中的一種疾病同時(shí)會(huì)引起該類疾病中的其它疾病。通過對T184副作用基因網(wǎng)絡(luò)的構(gòu)建和基因功能的分析,結(jié)果表明同一模塊中的基因具有相同的功能特性,同時(shí)與某一類別的癥狀相關(guān)聯(lián),得到?jīng)Q定癥狀類副作用的相關(guān)基因。通過對以副作用為中心的不同分子網(wǎng)絡(luò)的研究,對副作用與疾病、基因、癥狀之間的關(guān)系有了更深一步的認(rèn)識(shí),為研究副作用的微觀機(jī)理提供了幫助。
[Abstract]:Drug side effect is the patient in the course of treatment according to the normal dosage of drugs for diagnosis, prevention, treatment of a disease and the treatment of non-response to the purpose of treatment, generally will cause discomfort and pain of the patient.A U.S. study of drug side effects shows that severe side effects are the fourth leading cause of death in humans, causing 1000000 deaths a year.Therefore, side effects of drugs have gradually become the main problem of public health, which is not only the main reason of drug development failure, but also the main resistance of new drug research and production.At present, the research on drug side effects is mainly focused on the following three aspects: first, using the targets of drugs to predict drug side effects; second, predicting drug side effects based on the chemical structure of drugs; third,The data mining technology is used to mine the relationship between drugs and side effects from the document library to enrich and perfect the existing database.However, studies of drug side effects have only considered the characteristics of the drug itself and have not explored the relationship between disease, genes, symptoms and drug side effects.Therefore, the main content of this paper has the following three points.Firstly, we integrate the data from different databases to form the relationship data between side effects and diseases, genes, symptoms and so on, and establish the network of diseases based on genes, disease networks based on side effects, disease networks based on symptoms and so on.In order to explore the relationship between side effects and diseases, BGLL and BigCLAM(Cluster Affiliation Model for Big Networks (BigCLAM) are used to divide disease networks based on side effects.The results of the two algorithms have a high degree of modularity and a high degree of consistency, which indicates that there is a correlation between disease and side effects.Secondly, the relationship between disease and side effects was verified by enrichment analysis and gene consistency analysis.Through the network community analysis of the disease network with high similarity, 441 modules were obtained, and the disease module and disease classification were analyzed by the enrichment analysis method. The experiment shows that the disease in the same module is enriched to the same disease category.Gene consistency analysis is based on genetic disease network data and side-effect disease network data to calculate the overlapping characteristics of the two networks under both experimental and random conditions.The results show that the overlap property of the network under experimental conditions is much higher than that of random conditions, which verifies the relationship between disease and side effects.Then support vector machine (SVM) and simple logic regression were used to classify diseases with side effects (C01) as an example.Different classification methods can obtain higher precision, recall rate and AUC(AreaUnderroc current, the results show that some side effects may be the attribute of the disease itself.Finally, from the category of side effects, we found that side effects mainly belong to the category of T 047 disease and the category of symptoms of T 184.For the network topology characteristics of T047 side effects, such as degree distribution, it is found that most diseases associated with the same node belong to the same category.The results show that one disease in one disease can cause other diseases in the same time.Through the construction of T184 side effect gene network and the analysis of gene function, the results show that the genes in the same module have the same functional characteristics, and at the same time associated with a certain type of symptoms, the related genes that determine the symptom side effects are obtained.Through the study of different molecular networks centered on side effects, a deeper understanding of the relationship between side effects and diseases, genes, and symptoms has been obtained, which will help to study the microcosmic mechanism of side effects.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R96;O157.5

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