基于SVM的化合物致突變性分類預(yù)測系統(tǒng)的研究與實現(xiàn)
發(fā)布時間：2018-12-25 15:09
【摘要】：隨著科技的進步,越來越多的藥物被研發(fā)出來對抗各種疾病,而藥物研發(fā)的過程需要花費大量的物力、人力,且研發(fā)周期長。藥物研發(fā)的整個過程中需要考慮該藥物的ADMET(吸收,分布,代謝,排泄,毒性)五個特性,其中藥物毒性中的致突變性與癌癥密切相關(guān)。在藥物研發(fā)的最后階段動物人體試驗階段,會對藥物的對人體的致突變型進行測試,而在這個階段往往有很多藥物的致突變性實驗結(jié)果會因為對動物或者人體損害太大而被放棄研發(fā),這樣便浪費了之前各個階段的資金投入。近年來,計算機技術(shù)中的模式識別技術(shù)迅速發(fā)展,被應(yīng)用到社會上的各個領(lǐng)域,生物信息和藥物研發(fā)也是模式識別的一個重要研究方向。本系統(tǒng)的主要功能是利用機器學(xué)習(xí)算法對化合物的致突變性進行預(yù)測分類,通過分類模型對化合物致突變性相關(guān)的化合物特征進行分析。系統(tǒng)提供大量的化合物以及化合物的特征屬性作為分類模型的訓(xùn)練集,其中包括各研究機構(gòu)對該化合物致突變性研究的結(jié)果。系統(tǒng)為用戶提供化合物特征計算、特征選擇、數(shù)據(jù)清洗、分類模型建立、化合物致突變性預(yù)測、結(jié)果分析、結(jié)果文件保存等功能。研究人員可以根據(jù)預(yù)測結(jié)果來分析影響化合物致突變性的關(guān)鍵特征。系統(tǒng)使用Java語言開發(fā),使用的Spring MVC框架進行系統(tǒng)架構(gòu),使用MySQL數(shù)據(jù)庫進行化合物特征和個人信息等數(shù)據(jù)的存儲,實現(xiàn)了數(shù)據(jù)處理模塊、預(yù)測分類模塊、結(jié)果分析模塊、系統(tǒng)管理模塊和個人信息模塊。在數(shù)據(jù)處理模塊中,系統(tǒng)根據(jù)化合物的SMILES序列計算出化合物的1446緯化合物特征描述符并對特征數(shù)據(jù)進行缺失值處理和規(guī)范化等操作,再使用信息增益、CFS和Relief等特征選擇算法來對特征進行降維處理。在預(yù)測分類模塊中,系統(tǒng)采用支持向量機算法模型再使用Adaboost算法對支持向量機模型進行迭代提升,從而提高系統(tǒng)的預(yù)測準(zhǔn)度。經(jīng)過各種交叉驗證和獨立測試集驗證,系統(tǒng)能夠較為準(zhǔn)確的預(yù)測化合物的致突變性,精確度達(dá)到83.5%。在功能上和性能上都能滿足用戶的需求,達(dá)到預(yù)期研究效果。
[Abstract]:With the development of science and technology, more and more drugs are developed to fight various diseases, and the process of drug development takes a lot of material resources, manpower, and long research and development cycle. Five characteristics of ADMET (absorption, distribution, metabolism, excretion and toxicity) should be considered in the whole process of drug development. The mutagenicity of drug toxicity is closely related to cancer. In the final phase of drug development in animal human trials, the mutagenicity of drugs in humans will be tested. At this stage, many drug mutagenicity experiments will be abandoned because of too much damage to animals or human beings, thus wasting the previous stages of investment. In recent years, the pattern recognition technology in computer technology has developed rapidly and been applied to all fields of society. Biological information and drug development are also an important research direction of pattern recognition. The main function of this system is to predict and classify the mutagenicity of compounds by using machine learning algorithm, and to analyze the characteristics of compounds related to mutagenicity by classification model. The system provides a large number of compounds and their characteristic attributes as training sets for classification models, including the results of the studies on the mutagenicity of the compounds by various research institutions. The system provides users with functions such as compound feature calculation, feature selection, data cleaning, classification model building, compound mutagenicity prediction, result analysis, result file preservation, and so on. The researchers can use the predicted results to analyze key characteristics that affect mutagenicity of compounds. The system uses Java language to develop, uses the Spring MVC frame to carry on the system structure, uses the MySQL database to carry on the compound characteristic and the personal information and so on data storage, has realized the data processing module, the forecast classification module, the result analysis module, System management module and personal information module. In the data processing module, according to the SMILES sequence of the compound, the system calculates the characteristic descriptor of the compound in 1446 latitudes, processes the missing value and normalizes the characteristic data, and then uses the information gain. Feature selection algorithms such as CFS and Relief are used to reduce the dimension of features. In the prediction classification module, the support vector machine (SVM) algorithm model is adopted and the Adaboost algorithm is used to iterate the SVM model to improve the prediction accuracy of the system. After a variety of cross-validation and independent test set verification, the system can accurately predict the mutagenicity of compounds, with an accuracy of 83.555. In function and performance can meet the needs of users, to achieve the desired results.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TQ460;TP181
本文編號：2391291
[Abstract]:With the development of science and technology, more and more drugs are developed to fight various diseases, and the process of drug development takes a lot of material resources, manpower, and long research and development cycle. Five characteristics of ADMET (absorption, distribution, metabolism, excretion and toxicity) should be considered in the whole process of drug development. The mutagenicity of drug toxicity is closely related to cancer. In the final phase of drug development in animal human trials, the mutagenicity of drugs in humans will be tested. At this stage, many drug mutagenicity experiments will be abandoned because of too much damage to animals or human beings, thus wasting the previous stages of investment. In recent years, the pattern recognition technology in computer technology has developed rapidly and been applied to all fields of society. Biological information and drug development are also an important research direction of pattern recognition. The main function of this system is to predict and classify the mutagenicity of compounds by using machine learning algorithm, and to analyze the characteristics of compounds related to mutagenicity by classification model. The system provides a large number of compounds and their characteristic attributes as training sets for classification models, including the results of the studies on the mutagenicity of the compounds by various research institutions. The system provides users with functions such as compound feature calculation, feature selection, data cleaning, classification model building, compound mutagenicity prediction, result analysis, result file preservation, and so on. The researchers can use the predicted results to analyze key characteristics that affect mutagenicity of compounds. The system uses Java language to develop, uses the Spring MVC frame to carry on the system structure, uses the MySQL database to carry on the compound characteristic and the personal information and so on data storage, has realized the data processing module, the forecast classification module, the result analysis module, System management module and personal information module. In the data processing module, according to the SMILES sequence of the compound, the system calculates the characteristic descriptor of the compound in 1446 latitudes, processes the missing value and normalizes the characteristic data, and then uses the information gain. Feature selection algorithms such as CFS and Relief are used to reduce the dimension of features. In the prediction classification module, the support vector machine (SVM) algorithm model is adopted and the Adaboost algorithm is used to iterate the SVM model to improve the prediction accuracy of the system. After a variety of cross-validation and independent test set verification, the system can accurately predict the mutagenicity of compounds, with an accuracy of 83.555. In function and performance can meet the needs of users, to achieve the desired results.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TQ460;TP181
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