Multilabel and Missing Label Methods for Binary Quantitative Structure–Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions-Reference-Cited by-同舟云学术

Multilabel and Missing Label Methods for Binary Quantitative Structure–Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions

Published:2019-09-12 Issue:10 Volume:59 Page:4120-4130
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Pérez-Parras Toledano José¹,García-Pedrajas Nicolás¹,Cerruela-García Gonzalo¹^ORCID

Affiliation:

1. University of Córdoba, Department of Computing and Numerical Analysis, Campus de Rabanales, Albert Einstein Building, E-14071 Córdoba, Spain

Funder

Ministerio de Ciencia e Innovaci?n

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00611

Reference61 articles.

1. Machine Learning in Drug Discovery

2. QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models

3. Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers

4. Machine Learning in Virtual Screening

5. Predicting drug side effects by multi-label learning and ensemble learning

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