AutoTransOP: translating omics signatures without orthologue requirements using deep learning
Abstract The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predomina...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-024-00341-9 |
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| _version_ | 1846121823795675136 |
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| author | Nikolaos Meimetis Krista M. Pullen Daniel Y. Zhu Avlant Nilsson Trong Nghia Hoang Sara Magliacane Douglas A. Lauffenburger |
| author_facet | Nikolaos Meimetis Krista M. Pullen Daniel Y. Zhu Avlant Nilsson Trong Nghia Hoang Sara Magliacane Douglas A. Lauffenburger |
| author_sort | Nikolaos Meimetis |
| collection | DOAJ |
| description | Abstract The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts—most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist. |
| format | Article |
| id | doaj-art-d709836e3f53497f9d22e8848afa7d5b |
| institution | Kabale University |
| issn | 2056-7189 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-d709836e3f53497f9d22e8848afa7d5b2024-12-15T12:09:51ZengNature Portfolionpj Systems Biology and Applications2056-71892024-01-0110111910.1038/s41540-024-00341-9AutoTransOP: translating omics signatures without orthologue requirements using deep learningNikolaos Meimetis0Krista M. Pullen1Daniel Y. Zhu2Avlant Nilsson3Trong Nghia Hoang4Sara Magliacane5Douglas A. Lauffenburger6Department of Biological Engineering, Massachusetts Institute of TechnologyDepartment of Biological Engineering, Massachusetts Institute of TechnologyDepartment of Biological Engineering, Massachusetts Institute of TechnologyDepartment of Biological Engineering, Massachusetts Institute of TechnologySchool of Electrical Engineering and Computer Science, Washington State UniversityInstitute of Informatics, University of AmsterdamDepartment of Biological Engineering, Massachusetts Institute of TechnologyAbstract The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts—most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.https://doi.org/10.1038/s41540-024-00341-9 |
| spellingShingle | Nikolaos Meimetis Krista M. Pullen Daniel Y. Zhu Avlant Nilsson Trong Nghia Hoang Sara Magliacane Douglas A. Lauffenburger AutoTransOP: translating omics signatures without orthologue requirements using deep learning npj Systems Biology and Applications |
| title | AutoTransOP: translating omics signatures without orthologue requirements using deep learning |
| title_full | AutoTransOP: translating omics signatures without orthologue requirements using deep learning |
| title_fullStr | AutoTransOP: translating omics signatures without orthologue requirements using deep learning |
| title_full_unstemmed | AutoTransOP: translating omics signatures without orthologue requirements using deep learning |
| title_short | AutoTransOP: translating omics signatures without orthologue requirements using deep learning |
| title_sort | autotransop translating omics signatures without orthologue requirements using deep learning |
| url | https://doi.org/10.1038/s41540-024-00341-9 |
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