Hybrid attentional memory network for computational drug repositioning
Abstract Background Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining...
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| Format: | Article |
| Language: | English |
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BMC
2020-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-020-03898-4 |
| _version_ | 1819058586717782016 |
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| author | Jieyue He Xinxing Yang Zhuo Gong lbrahim Zamit |
| author_facet | Jieyue He Xinxing Yang Zhuo Gong lbrahim Zamit |
| author_sort | Jieyue He |
| collection | DOAJ |
| description | Abstract Background Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining the local information contained in few strong drug–disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug–disease associations. Few researchers have combined these two types of CF models to derive a hybrid model which can offer the advantages of both. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Results Inspired by the memory network, we propose the hybrid attentional memory network (HAMN) model, a deep architecture combining two classes of CF models in a nonlinear manner. First, the memory unit and the attention mechanism are combined to generate a neighborhood contribution representation to capture the local structure of few strong drug–disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug–disease associations. During this process, ancillary information of drugs and diseases can help alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is coupled with the drug latent factor and disease latent factor to produce predicted values. Comprehensive experimental results on two data sets demonstrate that our proposed HAMN model outperforms other comparison models based on the AUC, AUPR and HR indicators. Conclusions Through the performance on two drug repositioning data sets, we believe that the HAMN model proposes a new solution to improve the prediction accuracy of drug–disease associations and give pharmaceutical personnel a new perspective to develop new drugs. |
| first_indexed | 2024-12-21T13:57:34Z |
| format | Article |
| id | doaj.art-76689315421d46529d58211c0d1b444b |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T13:57:34Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-76689315421d46529d58211c0d1b444b2022-12-21T19:01:29ZengBMCBMC Bioinformatics1471-21052020-12-0121111710.1186/s12859-020-03898-4Hybrid attentional memory network for computational drug repositioningJieyue He0Xinxing Yang1Zhuo Gong2lbrahim Zamit3School of Computer Science and Engineering, Key Lab of Computer Network and Information Integration, MOE, Southeast UniversitySchool of Computer Science and Engineering, Key Lab of Computer Network and Information Integration, MOE, Southeast UniversitySchool of Computer Science and Engineering, Key Lab of Computer Network and Information Integration, MOE, Southeast UniversitySchool of Computer Science and Engineering, Key Lab of Computer Network and Information Integration, MOE, Southeast UniversityAbstract Background Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining the local information contained in few strong drug–disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug–disease associations. Few researchers have combined these two types of CF models to derive a hybrid model which can offer the advantages of both. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Results Inspired by the memory network, we propose the hybrid attentional memory network (HAMN) model, a deep architecture combining two classes of CF models in a nonlinear manner. First, the memory unit and the attention mechanism are combined to generate a neighborhood contribution representation to capture the local structure of few strong drug–disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug–disease associations. During this process, ancillary information of drugs and diseases can help alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is coupled with the drug latent factor and disease latent factor to produce predicted values. Comprehensive experimental results on two data sets demonstrate that our proposed HAMN model outperforms other comparison models based on the AUC, AUPR and HR indicators. Conclusions Through the performance on two drug repositioning data sets, we believe that the HAMN model proposes a new solution to improve the prediction accuracy of drug–disease associations and give pharmaceutical personnel a new perspective to develop new drugs.https://doi.org/10.1186/s12859-020-03898-4Drug repositioningData miningMemory networkAttention mechanism |
| spellingShingle | Jieyue He Xinxing Yang Zhuo Gong lbrahim Zamit Hybrid attentional memory network for computational drug repositioning BMC Bioinformatics Drug repositioning Data mining Memory network Attention mechanism |
| title | Hybrid attentional memory network for computational drug repositioning |
| title_full | Hybrid attentional memory network for computational drug repositioning |
| title_fullStr | Hybrid attentional memory network for computational drug repositioning |
| title_full_unstemmed | Hybrid attentional memory network for computational drug repositioning |
| title_short | Hybrid attentional memory network for computational drug repositioning |
| title_sort | hybrid attentional memory network for computational drug repositioning |
| topic | Drug repositioning Data mining Memory network Attention mechanism |
| url | https://doi.org/10.1186/s12859-020-03898-4 |
| work_keys_str_mv | AT jieyuehe hybridattentionalmemorynetworkforcomputationaldrugrepositioning AT xinxingyang hybridattentionalmemorynetworkforcomputationaldrugrepositioning AT zhuogong hybridattentionalmemorynetworkforcomputationaldrugrepositioning AT lbrahimzamit hybridattentionalmemorynetworkforcomputationaldrugrepositioning |