BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimization
IntroductionPredicting potential associations between microbes and drugs is crucial for advancing pharmaceutical research and development. In this manuscript, we introduced an innovative computational model named BANNMDA by integrating Bilinear Attention Networks(BAN) with the Nuclear Norm Minimizat...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1497886/full |
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| author | Mingmin Liang Xianzhi Liu Juncai Li Qijia Chen Bin Zeng Zhong Wang Jing Li Lei Wang |
| author_facet | Mingmin Liang Xianzhi Liu Juncai Li Qijia Chen Bin Zeng Zhong Wang Jing Li Lei Wang |
| author_sort | Mingmin Liang |
| collection | DOAJ |
| description | IntroductionPredicting potential associations between microbes and drugs is crucial for advancing pharmaceutical research and development. In this manuscript, we introduced an innovative computational model named BANNMDA by integrating Bilinear Attention Networks(BAN) with the Nuclear Norm Minimization (NNM) to uncover hidden connections between microbes and drugs.MethodsIn BANNMDA, we initially constructed a heterogeneous microbe-drug network by combining multiple drug and microbe similarity metrics with known microbe-drug relationships. Subsequently, we applied both BAN and NNM to compute predicted scores of potential microbe-drug associations. Finally, we implemented 5-fold cross-validation frameworks to evaluate the prediction performance of BANNMDA.Results and discussionThe experimental results indicated that BANNMDA outperformed state-of-the-art competitive methods. We conducted case studies on well-known drugs such as the Amoxicillin and Ceftazidime, as well as on pathogens such as Bacillus cereus and Influenza A virus, to further evaluate the efficacy of BANNMDA, and experimental outcomes showed that there were 9 out of the top 10 predicted drugs, along with 8 and 9 out of the top 10 predicted microbes having been corroborated by relevant literatures. These findings underscored the capability of BANNMDA to achieve commendable predictive accuracy. |
| format | Article |
| id | doaj-art-ec0c19916f02485ea523b4cccd79476b |
| institution | Kabale University |
| issn | 1664-302X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj-art-ec0c19916f02485ea523b4cccd79476b2025-01-22T07:14:45ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2025-01-011510.3389/fmicb.2024.14978861497886BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimizationMingmin Liang0Xianzhi Liu1Juncai Li2Qijia Chen3Bin Zeng4Zhong Wang5Jing Li6Lei Wang7School of Intelligent Equipment, Hunan Vocational College of Electronic and Technology, Changsha, ChinaSchool of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, ChinaSchool of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, ChinaSchool of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, ChinaSchool of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, ChinaSchool of Humanities and Education, Hunan Vocational College of Electronic and Technology, Changsha, ChinaSchool of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, ChinaBig Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, ChinaIntroductionPredicting potential associations between microbes and drugs is crucial for advancing pharmaceutical research and development. In this manuscript, we introduced an innovative computational model named BANNMDA by integrating Bilinear Attention Networks(BAN) with the Nuclear Norm Minimization (NNM) to uncover hidden connections between microbes and drugs.MethodsIn BANNMDA, we initially constructed a heterogeneous microbe-drug network by combining multiple drug and microbe similarity metrics with known microbe-drug relationships. Subsequently, we applied both BAN and NNM to compute predicted scores of potential microbe-drug associations. Finally, we implemented 5-fold cross-validation frameworks to evaluate the prediction performance of BANNMDA.Results and discussionThe experimental results indicated that BANNMDA outperformed state-of-the-art competitive methods. We conducted case studies on well-known drugs such as the Amoxicillin and Ceftazidime, as well as on pathogens such as Bacillus cereus and Influenza A virus, to further evaluate the efficacy of BANNMDA, and experimental outcomes showed that there were 9 out of the top 10 predicted drugs, along with 8 and 9 out of the top 10 predicted microbes having been corroborated by relevant literatures. These findings underscored the capability of BANNMDA to achieve commendable predictive accuracy.https://www.frontiersin.org/articles/10.3389/fmicb.2024.1497886/fullcomputational modelmicrobe–drug associationsbilinear attention networksnuclear norm minimizationprediction |
| spellingShingle | Mingmin Liang Xianzhi Liu Juncai Li Qijia Chen Bin Zeng Zhong Wang Jing Li Lei Wang BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimization Frontiers in Microbiology computational model microbe–drug associations bilinear attention networks nuclear norm minimization prediction |
| title | BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimization |
| title_full | BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimization |
| title_fullStr | BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimization |
| title_full_unstemmed | BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimization |
| title_short | BANNMDA: a computational model for predicting potential microbe–drug associations based on bilinear attention networks and nuclear norm minimization |
| title_sort | bannmda a computational model for predicting potential microbe drug associations based on bilinear attention networks and nuclear norm minimization |
| topic | computational model microbe–drug associations bilinear attention networks nuclear norm minimization prediction |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1497886/full |
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