Yan Hu 1 , Yi Lu 1 , Shuo Wang 1 , Mengying Zhang 1 , Xiaosheng Qu 2 , Bing Niu 1 Show Affiliations »
Abstract
BACKGROUND: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. OBJECTIVE: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. RESULTS: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. CONCLUSION: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
BACKGROUND: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. OBJECTIVE: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. RESULTS: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. CONCLUSION: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Entities: Disease
Species
Keywords:
Machine learning (ML); anticancer drugs; deep learning; k-nearest neighbor (kNN); linear discriminant analysis (LDA); naïve bayes (NB); principal components analysiszzm321990(PCA); random forest (RF); support vector machine (SVM); webzzm321990servers.
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Year: 2019
PMID： 30091413 DOI： 10.2174/1389450119666180809122244
Source DB: PubMed Journal: Curr Drug Targets ISSN： 1389-4501 Impact factor: 3.465