Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs.

Hu, Yan; Lu, Yi; Wang, Shuo; Zhang, Mengying; Qu, Xiaosheng; Niu, Bing

Hu, Yan; Lu, Yi; Wang, Shuo; Zhang, Mengying; Qu, Xiaosheng; Niu, Bing.

Affiliation

Hu Y; School of Life Sciences, Shanghai University, Shanghai 200444, China.
Lu Y; School of Life Sciences, Shanghai University, Shanghai 200444, China.
Wang S; School of Life Sciences, Shanghai University, Shanghai 200444, China.
Zhang M; School of Life Sciences, Shanghai University, Shanghai 200444, China.
Qu X; National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, 530023,Nanning, China.
Niu B; School of Life Sciences, Shanghai University, Shanghai 200444, China.

Curr Drug Targets ; 20(5): 488-500, 2019.

Article in En | MEDLINE | ID: mdl-30091413

Subject(s)

Antineoplastic Agents/chemical synthesis; Computational Biology/methods; Algorithms; Antineoplastic Agents/chemistry; Antineoplastic Agents/pharmacology; Bayes Theorem; Computational Biology/economics; Discriminant Analysis; Drug Design; Humans; Principal Component Analysis; Structure-Activity Relationship; Support Vector Machine

Key words

Machine learning (ML); anticancer drugs; deep learning; k-nearest neighbor (kNN); linear discriminant analysis (LDA); naïve bayes (NB); principal components analysis (PCA); random forest (RF); support vector machine (SVM); web servers.

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Computational Biology / Antineoplastic Agents Type of study: Prognostic_studies Limits: Humans Language: En Journal: Curr Drug Targets Journal subject: TERAPIA POR MEDICAMENTOS Year: 2019 Document type: Article Affiliation country: China Country of publication: United Arab Emirates

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