Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization
Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types,...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00218/full |
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| author | Feng Huang Yang Qiu Qiaojun Li Qiaojun Li Shichao Liu Shichao Liu Fuchuan Ni Fuchuan Ni |
| author_facet | Feng Huang Yang Qiu Qiaojun Li Qiaojun Li Shichao Liu Shichao Liu Fuchuan Ni Fuchuan Ni |
| author_sort | Feng Huang |
| collection | DOAJ |
| description | Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types, such as drug indications and drug side effects. However, the existing models for predicting drug-disease associations merely concentrate on independent tasks: recommending novel indications to benefit drug repositioning, predicting potential side effects to prevent drug-induced risk, or only determining the existence of drug-disease association. They ignore crucial prior knowledge of the correlations between different association types. Since the Comparative Toxicogenomics Database (CTD) annotates the drug-disease associations as therapeutic or marker/mechanism, we consider predicting the two types of association. To this end, we propose a collective matrix factorization-based multi-task learning method (CMFMTL) in this paper. CMFMTL handles the problem as multi-task learning where each task is to predict one type of association, and two tasks complement and improve each other by capturing the relatedness between them. First, drug-disease associations are represented as a bipartite network with two types of links representing therapeutic effects and non-therapeutic effects. Then, CMFMTL, respectively, approximates the association matrix regarding each link type by matrix tri-factorization, and shares the low-dimensional latent representations for drugs and diseases in the two related tasks for the goal of collective learning. Finally, CMFMTL puts the two tasks into a unified framework and an efficient algorithm is developed to solve our proposed optimization problem. In the computational experiments, CMFMTL outperforms several state-of-the-art methods both in the two tasks. Moreover, case studies show that CMFMTL helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their association types. |
| first_indexed | 2024-04-12T07:04:22Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-12T07:04:22Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-fc965ad2e104402da51d60c05d05ef4b2022-12-22T03:42:54ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-04-01810.3389/fbioe.2020.00218520425Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix FactorizationFeng Huang0Yang Qiu1Qiaojun Li2Qiaojun Li3Shichao Liu4Shichao Liu5Fuchuan Ni6Fuchuan Ni7College of Informatics, Huazhong Agricultural University, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaSchool of Electronic and Information Engineering, Henan Polytechnic Institute, Henan Nanyang, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaHubei Engineering Technology Research Center of Agricultural Big Data, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaHubei Engineering Technology Research Center of Agricultural Big Data, Wuhan, ChinaIdentifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types, such as drug indications and drug side effects. However, the existing models for predicting drug-disease associations merely concentrate on independent tasks: recommending novel indications to benefit drug repositioning, predicting potential side effects to prevent drug-induced risk, or only determining the existence of drug-disease association. They ignore crucial prior knowledge of the correlations between different association types. Since the Comparative Toxicogenomics Database (CTD) annotates the drug-disease associations as therapeutic or marker/mechanism, we consider predicting the two types of association. To this end, we propose a collective matrix factorization-based multi-task learning method (CMFMTL) in this paper. CMFMTL handles the problem as multi-task learning where each task is to predict one type of association, and two tasks complement and improve each other by capturing the relatedness between them. First, drug-disease associations are represented as a bipartite network with two types of links representing therapeutic effects and non-therapeutic effects. Then, CMFMTL, respectively, approximates the association matrix regarding each link type by matrix tri-factorization, and shares the low-dimensional latent representations for drugs and diseases in the two related tasks for the goal of collective learning. Finally, CMFMTL puts the two tasks into a unified framework and an efficient algorithm is developed to solve our proposed optimization problem. In the computational experiments, CMFMTL outperforms several state-of-the-art methods both in the two tasks. Moreover, case studies show that CMFMTL helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their association types.https://www.frontiersin.org/article/10.3389/fbioe.2020.00218/fulldrug-disease associationpredicting association typesimilaritycollective matrix factorizationmulti-task learning |
| spellingShingle | Feng Huang Yang Qiu Qiaojun Li Qiaojun Li Shichao Liu Shichao Liu Fuchuan Ni Fuchuan Ni Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization Frontiers in Bioengineering and Biotechnology drug-disease association predicting association type similarity collective matrix factorization multi-task learning |
| title | Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization |
| title_full | Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization |
| title_fullStr | Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization |
| title_full_unstemmed | Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization |
| title_short | Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization |
| title_sort | predicting drug disease associations via multi task learning based on collective matrix factorization |
| topic | drug-disease association predicting association type similarity collective matrix factorization multi-task learning |
| url | https://www.frontiersin.org/article/10.3389/fbioe.2020.00218/full |
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