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Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity relationship (QSAR) and quantitative structur...

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Autores principales: Chen, Chia-Hsiu, Tanaka, Kenichi, Kotera, Masaaki, Funatsu, Kimito
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106596/
https://www.ncbi.nlm.nih.gov/pubmed/33430997
http://dx.doi.org/10.1186/s13321-020-0417-9
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author Chen, Chia-Hsiu
Tanaka, Kenichi
Kotera, Masaaki
Funatsu, Kimito
author_facet Chen, Chia-Hsiu
Tanaka, Kenichi
Kotera, Masaaki
Funatsu, Kimito
author_sort Chen, Chia-Hsiu
collection PubMed
description Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine’s inability to interpret the predictions to researchers. In fact, many implementations of ensemble learning models are able to quantify the overall magnitude of each feature. For example, feature importance allows us to assess the relative importance of features and to interpret the predictions. However, different ensemble learning methods or implementations may lead to different feature selections for interpretation. In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting) for regression and binary classification modeling tasks. Then, the blending methods were built by summarizing four different ensemble learning methods. The blending method led to better performance and a unification interpretation by summarizing individual predictions from different learning models. The important features of two case studies which gave us some valuable information to compound properties were discussed in detail in this report. QSPR modeling with interpretable machine learning techniques can move the chemical design forward to work more efficiently, confirm hypothesis and establish knowledge for better results.
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spelling pubmed-71065962020-04-03 Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications Chen, Chia-Hsiu Tanaka, Kenichi Kotera, Masaaki Funatsu, Kimito J Cheminform Research Article Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine’s inability to interpret the predictions to researchers. In fact, many implementations of ensemble learning models are able to quantify the overall magnitude of each feature. For example, feature importance allows us to assess the relative importance of features and to interpret the predictions. However, different ensemble learning methods or implementations may lead to different feature selections for interpretation. In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting) for regression and binary classification modeling tasks. Then, the blending methods were built by summarizing four different ensemble learning methods. The blending method led to better performance and a unification interpretation by summarizing individual predictions from different learning models. The important features of two case studies which gave us some valuable information to compound properties were discussed in detail in this report. QSPR modeling with interpretable machine learning techniques can move the chemical design forward to work more efficiently, confirm hypothesis and establish knowledge for better results. Springer International Publishing 2020-03-30 /pmc/articles/PMC7106596/ /pubmed/33430997 http://dx.doi.org/10.1186/s13321-020-0417-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Chen, Chia-Hsiu
Tanaka, Kenichi
Kotera, Masaaki
Funatsu, Kimito
Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
title Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
title_full Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
title_fullStr Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
title_full_unstemmed Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
title_short Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
title_sort comparison and improvement of the predictability and interpretability with ensemble learning models in qspr applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106596/
https://www.ncbi.nlm.nih.gov/pubmed/33430997
http://dx.doi.org/10.1186/s13321-020-0417-9
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