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MSDRP: a deep learning model based on multisource data for predicting drug response
MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict dru...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474952/ https://www.ncbi.nlm.nih.gov/pubmed/37606993 http://dx.doi.org/10.1093/bioinformatics/btad514 |
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author | Zhao, Haochen Zhang, Xiaoyu Zhao, Qichang Li, Yaohang Wang, Jianxin |
author_facet | Zhao, Haochen Zhang, Xiaoyu Zhao, Qichang Li, Yaohang Wang, Jianxin |
author_sort | Zhao, Haochen |
collection | PubMed |
description | MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines. RESULTS: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model. AVAILABILITY AND IMPLEMENTATION: The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP. |
format | Online Article Text |
id | pubmed-10474952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-104749522023-09-03 MSDRP: a deep learning model based on multisource data for predicting drug response Zhao, Haochen Zhang, Xiaoyu Zhao, Qichang Li, Yaohang Wang, Jianxin Bioinformatics Original Paper MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines. RESULTS: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model. AVAILABILITY AND IMPLEMENTATION: The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP. Oxford University Press 2023-08-22 /pmc/articles/PMC10474952/ /pubmed/37606993 http://dx.doi.org/10.1093/bioinformatics/btad514 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Zhao, Haochen Zhang, Xiaoyu Zhao, Qichang Li, Yaohang Wang, Jianxin MSDRP: a deep learning model based on multisource data for predicting drug response |
title | MSDRP: a deep learning model based on multisource data for predicting drug response |
title_full | MSDRP: a deep learning model based on multisource data for predicting drug response |
title_fullStr | MSDRP: a deep learning model based on multisource data for predicting drug response |
title_full_unstemmed | MSDRP: a deep learning model based on multisource data for predicting drug response |
title_short | MSDRP: a deep learning model based on multisource data for predicting drug response |
title_sort | msdrp: a deep learning model based on multisource data for predicting drug response |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474952/ https://www.ncbi.nlm.nih.gov/pubmed/37606993 http://dx.doi.org/10.1093/bioinformatics/btad514 |
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