NMGMDA: A Computational Model for Predicting Potential Microbe–Drug Associations based on Minimize Matrix Nuclear Norm and Graph Attention Network Article Swipe
Mingmin Liang
,
Xianzhi Liu
,
Qijia Chen
,
Bin Zeng
,
Lei Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3364690/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3364690/v1
For drug research and development, the probable microbe-drug associations can be predicted with considerable utility. Deep learning-based techniques have recently found widespread use in the biomedical industry and have significantly improved identification performance. Additionally, the growing body of knowledge on germs and pharmaceutical biomedicine offers a fantastic potential for methods based on deep learning to forecast hidden associations between microbes and drugs. In order to infer latent microbe-drug associations, we developed a unique computational model in this publication called NMGMDA based on the nuclear norm minimization and graph attention network. We created a heterogeneous microbe-drug network in NMGMDA by fusing the drug and microbe similarities with the established associations between drugs and microbes. Then, in order to get predicted scores of potential microbe-drug associations, we used the nuclear norm minimization approach and a GAT-based auto-encoder, respectively. The final results, which are based on two datasets and weighted average of these two predicted scores, demonstrated that NMGMDA can outperform state-of-the-art competitive approaches. Case studies further demonstrated its capacity to reliably find fresh associations.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3364690/v1
- https://www.researchsquare.com/article/rs-3364690/latest.pdf
- OA Status
- gold
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387186629
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387186629Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3364690/v1Digital Object Identifier
- Title
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NMGMDA: A Computational Model for Predicting Potential Microbe–Drug Associations based on Minimize Matrix Nuclear Norm and Graph Attention NetworkWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-29Full publication date if available
- Authors
-
Mingmin Liang, Xianzhi Liu, Qijia Chen, Bin Zeng, Lei WangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3364690/v1Publisher landing page
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https://www.researchsquare.com/article/rs-3364690/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-3364690/latest.pdfDirect OA link when available
- Concepts
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Drug, Norm (philosophy), Computer science, Artificial intelligence, Graph, Machine learning, Encoder, Biology, Theoretical computer science, Pharmacology, Law, Operating system, Political scienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.further | 164 |
| abstract_inverted_index.growing | 36 |
| abstract_inverted_index.methods | 50 |
| abstract_inverted_index.microbe | 104 |
| abstract_inverted_index.network | 96 |
| abstract_inverted_index.nuclear | 84, 128 |
| abstract_inverted_index.scores, | 153 |
| abstract_inverted_index.studies | 163 |
| abstract_inverted_index.approach | 131 |
| abstract_inverted_index.capacity | 167 |
| abstract_inverted_index.datasets | 145 |
| abstract_inverted_index.forecast | 56 |
| abstract_inverted_index.improved | 31 |
| abstract_inverted_index.industry | 27 |
| abstract_inverted_index.learning | 54 |
| abstract_inverted_index.microbes | 60 |
| abstract_inverted_index.network. | 90 |
| abstract_inverted_index.probable | 7 |
| abstract_inverted_index.recently | 20 |
| abstract_inverted_index.reliably | 169 |
| abstract_inverted_index.research | 3 |
| abstract_inverted_index.results, | 139 |
| abstract_inverted_index.utility. | 15 |
| abstract_inverted_index.weighted | 147 |
| abstract_inverted_index.GAT-based | 134 |
| abstract_inverted_index.attention | 89 |
| abstract_inverted_index.developed | 71 |
| abstract_inverted_index.fantastic | 47 |
| abstract_inverted_index.knowledge | 39 |
| abstract_inverted_index.microbes. | 113 |
| abstract_inverted_index.potential | 48, 122 |
| abstract_inverted_index.predicted | 12, 119, 152 |
| abstract_inverted_index.biomedical | 26 |
| abstract_inverted_index.outperform | 158 |
| abstract_inverted_index.techniques | 18 |
| abstract_inverted_index.widespread | 22 |
| abstract_inverted_index.approaches. | 161 |
| abstract_inverted_index.biomedicine | 44 |
| abstract_inverted_index.competitive | 160 |
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| abstract_inverted_index.associations | 9, 58, 109 |
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| abstract_inverted_index.demonstrated | 154, 165 |
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| abstract_inverted_index.minimization | 86, 130 |
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| abstract_inverted_index.similarities | 105 |
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| abstract_inverted_index.associations, | 69, 124 |
| abstract_inverted_index.associations. | 172 |
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| abstract_inverted_index.pharmaceutical | 43 |
| abstract_inverted_index.state-of-the-art | 159 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.3014272 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |