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Graph-Based Feature Selection Approach for Molecular Activity Prediction
[Image: see text] In the construction of QSAR models for the prediction of molecular activity, feature selection is a common task aimed at improving the results and understanding of the problem. The selection of features allows elimination of irrelevant and redundant features, reduces the effect of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006223/ https://www.ncbi.nlm.nih.gov/pubmed/35315648 http://dx.doi.org/10.1021/acs.jcim.1c01578 |
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author | Cerruela-García, Gonzalo Cuevas-Muñoz, José Manuel García-Pedrajas, Nicolás |
author_facet | Cerruela-García, Gonzalo Cuevas-Muñoz, José Manuel García-Pedrajas, Nicolás |
author_sort | Cerruela-García, Gonzalo |
collection | PubMed |
description | [Image: see text] In the construction of QSAR models for the prediction of molecular activity, feature selection is a common task aimed at improving the results and understanding of the problem. The selection of features allows elimination of irrelevant and redundant features, reduces the effect of dimensionality problems, and improves the generalization and interpretability of the models. In many feature selection applications, such as those based on ensembles of feature selectors, it is necessary to combine different selection processes. In this work, we evaluate the application of a new feature selection approach to the prediction of molecular activity, based on the construction of an undirected graph to combine base feature selectors. The experimental results demonstrate the efficiency of the graph-based method in terms of the classification performance, reduction, and redundancy compared to the standard voting method. The graph-based method can be extended to different feature selection algorithms and applied to other cheminformatics problems. |
format | Online Article Text |
id | pubmed-9006223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-90062232022-04-13 Graph-Based Feature Selection Approach for Molecular Activity Prediction Cerruela-García, Gonzalo Cuevas-Muñoz, José Manuel García-Pedrajas, Nicolás J Chem Inf Model [Image: see text] In the construction of QSAR models for the prediction of molecular activity, feature selection is a common task aimed at improving the results and understanding of the problem. The selection of features allows elimination of irrelevant and redundant features, reduces the effect of dimensionality problems, and improves the generalization and interpretability of the models. In many feature selection applications, such as those based on ensembles of feature selectors, it is necessary to combine different selection processes. In this work, we evaluate the application of a new feature selection approach to the prediction of molecular activity, based on the construction of an undirected graph to combine base feature selectors. The experimental results demonstrate the efficiency of the graph-based method in terms of the classification performance, reduction, and redundancy compared to the standard voting method. The graph-based method can be extended to different feature selection algorithms and applied to other cheminformatics problems. American Chemical Society 2022-03-22 2022-04-11 /pmc/articles/PMC9006223/ /pubmed/35315648 http://dx.doi.org/10.1021/acs.jcim.1c01578 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Cerruela-García, Gonzalo Cuevas-Muñoz, José Manuel García-Pedrajas, Nicolás Graph-Based Feature Selection Approach for Molecular Activity Prediction |
title | Graph-Based Feature Selection Approach for Molecular
Activity Prediction |
title_full | Graph-Based Feature Selection Approach for Molecular
Activity Prediction |
title_fullStr | Graph-Based Feature Selection Approach for Molecular
Activity Prediction |
title_full_unstemmed | Graph-Based Feature Selection Approach for Molecular
Activity Prediction |
title_short | Graph-Based Feature Selection Approach for Molecular
Activity Prediction |
title_sort | graph-based feature selection approach for molecular
activity prediction |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006223/ https://www.ncbi.nlm.nih.gov/pubmed/35315648 http://dx.doi.org/10.1021/acs.jcim.1c01578 |
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