An ensemble approach for in silico prediction of Ames mutagenicity
In this paper, we evaluate three learning algorithms based on supervised projections for molecular activity prediction. Using an approach based on supervised projections of the input space to construct ensembles of classifiers, three algorithms were tested. We constructed the projections by consider...
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| Veröffentlicht in: | Journal of mathematical chemistry 2018-08, Vol.56 (7), p.2085-2098 |
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| container_end_page | 2098 |
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| container_issue | 7 |
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| container_title | Journal of mathematical chemistry |
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| creator | Cerruela García, Gonzalo García-Pedrajas, Nicolás Luque Ruiz, Irene Gómez-Nieto, Miguel Ángel |
| description | In this paper, we evaluate three learning algorithms based on supervised projections for molecular activity prediction. Using an approach based on supervised projections of the input space to construct ensembles of classifiers, three algorithms were tested. We constructed the projections by considering only instances that were misclassified by a previous classifier using the hidden layer of an Artificial Neural Network. We applied a supervised linear projection of the input space using a Nonparametric Discriminant Analysis method. Finally, we projected onto a subspace that minimizes the weighted error for each step. Using these three methods to construct ensembles of classifiers for the in silico prediction of Ames mutagenicity, we demonstrated the improved behavior of our proposal compared to classical methods. |
| doi_str_mv | 10.1007/s10910-018-0855-z |
| format | Article |
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Using these three methods to construct ensembles of classifiers for the in silico prediction of Ames mutagenicity, we demonstrated the improved behavior of our proposal compared to classical methods.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Classifiers</subject><subject>Discriminant analysis</subject><subject>Forecasting</subject><subject>Machine learning</subject><subject>Math. 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| subjects | Algorithms Artificial neural networks Chemistry Chemistry and Materials Science Classifiers Discriminant analysis Forecasting Machine learning Math. Applications in Chemistry Molecular chains Mutagenicity Neural networks Original Paper Physical Chemistry Theoretical and Computational Chemistry |
| title | An ensemble approach for in silico prediction of Ames mutagenicity |
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