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Multi-view feature representation and fusion for drug-drug interactions prediction

BACKGROUND: Drug-drug interactions (DDIs) prediction is vital for pharmacology and clinical application to avoid adverse drug reactions on patients. It is challenging because DDIs are related to multiple factors, such as genes, drug molecular structure, diseases, biological processes, side effects,...

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Autores principales: Wang, Jing, Zhang, Shuo, Li, Runzhi, Chen, Gang, Yan, Siyu, Ma, Lihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015807/
https://www.ncbi.nlm.nih.gov/pubmed/36918766
http://dx.doi.org/10.1186/s12859-023-05212-4
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author Wang, Jing
Zhang, Shuo
Li, Runzhi
Chen, Gang
Yan, Siyu
Ma, Lihong
author_facet Wang, Jing
Zhang, Shuo
Li, Runzhi
Chen, Gang
Yan, Siyu
Ma, Lihong
author_sort Wang, Jing
collection PubMed
description BACKGROUND: Drug-drug interactions (DDIs) prediction is vital for pharmacology and clinical application to avoid adverse drug reactions on patients. It is challenging because DDIs are related to multiple factors, such as genes, drug molecular structure, diseases, biological processes, side effects, etc. It is a crucial technology for Knowledge graph to present multi-relation among entities. Recently some existing graph-based computation models have been proposed for DDIs prediction and get good performance. However, there are still some challenges in the knowledge graph representation, which can extract rich latent features from drug knowledge graph (KG). RESULTS: In this work, we propose a novel multi-view feature representation and fusion (MuFRF) architecture to realize DDIs prediction. It consists of two views of feature representation and a multi-level latent feature fusion. For the feature representation from the graph view and KG view, we use graph isomorphism network to map drug molecular structures and use RotatE to implement the vector representation on bio-medical knowledge graph, respectively. We design concatenate-level and scalar-level strategies in the multi-level latent feature fusion to capture latent features from drug molecular structure information and semantic features from bio-medical KG. And the multi-head attention mechanism achieves the optimization of features on binary and multi-class classification tasks. We evaluate our proposed method based on two open datasets in the experiments. Experiments indicate that MuFRF outperforms the classic and state-of-the-art models. CONCLUSIONS: Our proposed model can fully exploit and integrate the latent feature from the drug molecular structure graph (graph view) and rich bio-medical knowledge graph (KG view). We find that a multi-view feature representation and fusion model can accurately predict DDIs. It may contribute to providing with some guidance for research and validation for discovering novel DDIs.
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spelling pubmed-100158072023-03-16 Multi-view feature representation and fusion for drug-drug interactions prediction Wang, Jing Zhang, Shuo Li, Runzhi Chen, Gang Yan, Siyu Ma, Lihong BMC Bioinformatics Research BACKGROUND: Drug-drug interactions (DDIs) prediction is vital for pharmacology and clinical application to avoid adverse drug reactions on patients. It is challenging because DDIs are related to multiple factors, such as genes, drug molecular structure, diseases, biological processes, side effects, etc. It is a crucial technology for Knowledge graph to present multi-relation among entities. Recently some existing graph-based computation models have been proposed for DDIs prediction and get good performance. However, there are still some challenges in the knowledge graph representation, which can extract rich latent features from drug knowledge graph (KG). RESULTS: In this work, we propose a novel multi-view feature representation and fusion (MuFRF) architecture to realize DDIs prediction. It consists of two views of feature representation and a multi-level latent feature fusion. For the feature representation from the graph view and KG view, we use graph isomorphism network to map drug molecular structures and use RotatE to implement the vector representation on bio-medical knowledge graph, respectively. We design concatenate-level and scalar-level strategies in the multi-level latent feature fusion to capture latent features from drug molecular structure information and semantic features from bio-medical KG. And the multi-head attention mechanism achieves the optimization of features on binary and multi-class classification tasks. We evaluate our proposed method based on two open datasets in the experiments. Experiments indicate that MuFRF outperforms the classic and state-of-the-art models. CONCLUSIONS: Our proposed model can fully exploit and integrate the latent feature from the drug molecular structure graph (graph view) and rich bio-medical knowledge graph (KG view). We find that a multi-view feature representation and fusion model can accurately predict DDIs. It may contribute to providing with some guidance for research and validation for discovering novel DDIs. BioMed Central 2023-03-14 /pmc/articles/PMC10015807/ /pubmed/36918766 http://dx.doi.org/10.1186/s12859-023-05212-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Wang, Jing
Zhang, Shuo
Li, Runzhi
Chen, Gang
Yan, Siyu
Ma, Lihong
Multi-view feature representation and fusion for drug-drug interactions prediction
title Multi-view feature representation and fusion for drug-drug interactions prediction
title_full Multi-view feature representation and fusion for drug-drug interactions prediction
title_fullStr Multi-view feature representation and fusion for drug-drug interactions prediction
title_full_unstemmed Multi-view feature representation and fusion for drug-drug interactions prediction
title_short Multi-view feature representation and fusion for drug-drug interactions prediction
title_sort multi-view feature representation and fusion for drug-drug interactions prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015807/
https://www.ncbi.nlm.nih.gov/pubmed/36918766
http://dx.doi.org/10.1186/s12859-023-05212-4
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