Yan Hu 1 , Guangya Zhou 1 , Chi Zhang 2 , Mengying Zhang 1 , Qin Chen 1 , Linfeng Zheng 3,4 , Bing Niu 1 Show Affiliations »
Abstract
BACKGROUND: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer's drugs has become one of the most popular medical topics. METHODS: In this study, in order to build a predicting model for Alzheimer's drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. RESULTS: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. CONCLUSION: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer's drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
BACKGROUND: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer's drugs has become one of the most popular medical topics. METHODS: In this study, in order to build a predicting model for Alzheimer's drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR ) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. RESULTS: The prediction rates of correctness using Randomforest for AChE , BChE , MAO-B , BACE1 , Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. CONCLUSION: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer's drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD /. Furthermore, it can distinguish the target protein of a small molecule. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Entities: Disease
Gene
Species
Keywords:
AdaBoost; Alzheimer's disease; BP neural network (BP); BayesNet; C4.5; K nearest-neighbors (KNN); Machine learning (ML); random forest (RF); support vector machine (SVM); web server.
Mesh： See more »
Year: 2019
PMID： 30605059 DOI： 10.2174/1567205016666190103154855
Source DB: PubMed Journal: Curr Alzheimer Res ISSN： 1567-2050 Impact factor: 3.498