基于多特征融合的藥物作用模式統(tǒng)計(jì)分析方法研究
發(fā)布時(shí)間：2019-04-26 08:07
【摘要】：藥物作用模式(MoA)研究對(duì)于藥物設(shè)計(jì)研發(fā)、預(yù)測(cè)藥物未知副作用以及指導(dǎo)用藥具有重要意義。伴隨著新藥產(chǎn)率逐年呈下降趨勢(shì),預(yù)測(cè)已有藥物的潛在作用模式被認(rèn)為是解決新藥開發(fā)高投入低產(chǎn)出的有效方法之一。對(duì)已知藥物的作用模式進(jìn)行分析,可以幫助發(fā)現(xiàn)藥物可能的潛在應(yīng)用和未知的副作用。本文以美國(guó)食品與藥品監(jiān)督管理局(FDA)批準(zhǔn)的藥物為研究對(duì)象,通過統(tǒng)計(jì)分析與數(shù)據(jù)挖掘方法,分析各藥物信息與作用模式的關(guān)系,挖掘不同作用模式之間的藥物信息的差異性,結(jié)合多特征融合的概率集成方法預(yù)測(cè)潛在的藥物作用模式,為臨床指導(dǎo)用藥提供一定的參考依據(jù)。主要內(nèi)容如下:1.提出了一種利用藥物的化學(xué)結(jié)構(gòu)信息、生物特性、藥理學(xué)特性以及藥物副作用來系統(tǒng)分析評(píng)估藥物作用模式的方法。該方法主要使用了藥物信息為特征,通過統(tǒng)計(jì)分析方法,分析不同藥物作用模式下各種藥物特性信息的差異性及富集情況,以及驗(yàn)證這些特性特征是否可以較好的區(qū)分不同的藥物作用模式。實(shí)驗(yàn)結(jié)果表明,不同藥物作用模式類別下藥物的各個(gè)信息均具有顯著性差異,且這些藥物特性信息可以較好的區(qū)分不同的藥物作用模式。2.提出了一種基于多特征融合的概率集成方法構(gòu)建藥物作用模式網(wǎng)絡(luò)挖掘潛在的藥物作用模式。該方法基于貝葉斯網(wǎng)絡(luò)模型理論對(duì)藥物的四個(gè)特性的相似性特征進(jìn)行融合,進(jìn)而結(jié)合概率論知識(shí)構(gòu)建網(wǎng)絡(luò)挖掘潛在的藥物作用模式。實(shí)驗(yàn)結(jié)果表明,多特征融合對(duì)模型預(yù)測(cè)性能確實(shí)有效,通過對(duì)比其他四種不同的機(jī)器學(xué)習(xí)模型對(duì)藥物作用模式的預(yù)測(cè)性能發(fā)現(xiàn),概率集成方法預(yù)測(cè)精度最高,魯棒性較好,而且可以成功預(yù)測(cè)出一些潛在的藥物作用模式。3.構(gòu)建了一個(gè)在線藥物作用模式類別的藥理學(xué)數(shù)據(jù)庫(kù)"MoABank"。該數(shù)據(jù)庫(kù)可以提供較為全面的藥物作用模式類別及藥物的靶標(biāo)、通路及副作用等信息,以及本文模型的分析與預(yù)測(cè)結(jié)果。
[Abstract]:The study of drug action model (MoA) is of great significance for drug design and development, prediction of unknown side effects of drugs and guidance of drug use. As the yield of new drugs decreases year by year, predicting the potential action models of existing drugs is considered to be one of the effective methods to solve the problem of high input and low output in the development of new drugs. An analysis of the patterns of action of known drugs can help to identify potential applications and unknown side effects of drugs. In this paper, the drug approved by the Food and Drug Administration of the United States of America (FDA) as the research object, through statistical analysis and data mining methods, to analyze the relationship between drug information and action patterns. Mining the difference of drug information among different action patterns, combining the probability integration method of multi-feature fusion to predict the potential drug action patterns, which provides a certain reference basis for clinical guidance of drug use. The main contents are as follows: 1. This paper presents a method to systematically analyze and evaluate drug action patterns by using chemical structure information, biological characteristics, pharmacological properties and side effects of drugs. This method mainly uses the drug information as the characteristic, through the statistical analysis method, analyzes the difference and the enrichment of the various drug characteristic information under the different drug action mode. And verify whether these characteristics can better distinguish the different drug action patterns. The experimental results show that there are significant differences in the information of different drug types under different drug action modes, and these information of drug characteristics can be used to distinguish different drug action patterns. 2. A probability ensemble method based on multi-feature fusion is proposed to construct drug action patterns network mining potential drug action patterns. Based on Bayesian network model theory, the similarity characteristics of the four characteristics of drugs are fused, and then the potential drug action patterns are constructed by combining the knowledge of probability theory. The experimental results show that the multi-feature fusion is effective for the prediction performance of the model. By comparing the prediction performance of the other four different machine learning models to the drug action model, the probability ensemble method has the highest prediction accuracy and better robustness. And some potential drug action patterns can be predicted successfully. 3. A pharmacology database "MoABank" for online drug action model categories was constructed. The database can provide more comprehensive information about the types of drug action patterns, targets, pathways and side effects of drugs, as well as the results of analysis and prediction of the model in this paper.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R91
本文編號(hào)：2465896
[Abstract]:The study of drug action model (MoA) is of great significance for drug design and development, prediction of unknown side effects of drugs and guidance of drug use. As the yield of new drugs decreases year by year, predicting the potential action models of existing drugs is considered to be one of the effective methods to solve the problem of high input and low output in the development of new drugs. An analysis of the patterns of action of known drugs can help to identify potential applications and unknown side effects of drugs. In this paper, the drug approved by the Food and Drug Administration of the United States of America (FDA) as the research object, through statistical analysis and data mining methods, to analyze the relationship between drug information and action patterns. Mining the difference of drug information among different action patterns, combining the probability integration method of multi-feature fusion to predict the potential drug action patterns, which provides a certain reference basis for clinical guidance of drug use. The main contents are as follows: 1. This paper presents a method to systematically analyze and evaluate drug action patterns by using chemical structure information, biological characteristics, pharmacological properties and side effects of drugs. This method mainly uses the drug information as the characteristic, through the statistical analysis method, analyzes the difference and the enrichment of the various drug characteristic information under the different drug action mode. And verify whether these characteristics can better distinguish the different drug action patterns. The experimental results show that there are significant differences in the information of different drug types under different drug action modes, and these information of drug characteristics can be used to distinguish different drug action patterns. 2. A probability ensemble method based on multi-feature fusion is proposed to construct drug action patterns network mining potential drug action patterns. Based on Bayesian network model theory, the similarity characteristics of the four characteristics of drugs are fused, and then the potential drug action patterns are constructed by combining the knowledge of probability theory. The experimental results show that the multi-feature fusion is effective for the prediction performance of the model. By comparing the prediction performance of the other four different machine learning models to the drug action model, the probability ensemble method has the highest prediction accuracy and better robustness. And some potential drug action patterns can be predicted successfully. 3. A pharmacology database "MoABank" for online drug action model categories was constructed. The database can provide more comprehensive information about the types of drug action patterns, targets, pathways and side effects of drugs, as well as the results of analysis and prediction of the model in this paper.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R91
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 王志剛;陳鑫;謝麗芳;楊嘯林;張正國(guó);;藥物功能模式相似度及其聚類[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2011年06期
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