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In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning
In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the interrelation between tasks is known to be import...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6896155/ https://www.ncbi.nlm.nih.gov/pubmed/31703452 http://dx.doi.org/10.3390/genes10110906 |
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author | Lee, Kyoungyeul Kim, Dongsup |
author_facet | Lee, Kyoungyeul Kim, Dongsup |
author_sort | Lee, Kyoungyeul |
collection | PubMed |
description | In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the interrelation between tasks is known to be important for successful multi-task learning, its adverse effect has been underestimated. In this study, we used molecular interaction data of human targets from ChEMBL to train and test various multi-task and single-task networks and examined the effectiveness of multi-task learning for different compositions of targets. Targets were clustered based on sequence similarity in their binding domains and various target sets from clusters were chosen. By comparing the performance of deep neural architectures for each target set, we found that similarity within a target set is highly important for reliable multi-task learning. For a diverse target set or overall human targets, the performance of multi-task learning was lower than single-task learning, but outperformed single-task for the target set containing similar targets. From this insight, we developed Multiple Partial Multi-Task learning, which is suitable for binding prediction for human drug targets. |
format | Online Article Text |
id | pubmed-6896155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68961552019-12-23 In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning Lee, Kyoungyeul Kim, Dongsup Genes (Basel) Article In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the interrelation between tasks is known to be important for successful multi-task learning, its adverse effect has been underestimated. In this study, we used molecular interaction data of human targets from ChEMBL to train and test various multi-task and single-task networks and examined the effectiveness of multi-task learning for different compositions of targets. Targets were clustered based on sequence similarity in their binding domains and various target sets from clusters were chosen. By comparing the performance of deep neural architectures for each target set, we found that similarity within a target set is highly important for reliable multi-task learning. For a diverse target set or overall human targets, the performance of multi-task learning was lower than single-task learning, but outperformed single-task for the target set containing similar targets. From this insight, we developed Multiple Partial Multi-Task learning, which is suitable for binding prediction for human drug targets. MDPI 2019-11-07 /pmc/articles/PMC6896155/ /pubmed/31703452 http://dx.doi.org/10.3390/genes10110906 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Kyoungyeul Kim, Dongsup In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning |
title | In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning |
title_full | In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning |
title_fullStr | In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning |
title_full_unstemmed | In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning |
title_short | In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning |
title_sort | in-silico molecular binding prediction for human drug targets using deep neural multi-task learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6896155/ https://www.ncbi.nlm.nih.gov/pubmed/31703452 http://dx.doi.org/10.3390/genes10110906 |
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