An Approach for Predicting Protein-Protein Interactions using Supervised Autoencoders
Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen protei...
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| Vydané v: | Procedia computer science Ročník 207; s. 2023 - 2032 |
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Elsevier B.V
2022
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| Abstract | Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen proteins. Supervised autoencoders, which are neural networks trained to simultaneously predict labels and reconstruct their inputs, have been proved to offer generalization guarantees in the linear case. Moreover, the addition of a reconstruction branch was empirically shown to improve the performance of standard neural networks classifiers on several tasks. In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders. The proposed approach consists of initially training a denoising autoencoder on protein sequences, followed by a supervised training stage in which the model learns to both predict whether two proteins interact and to reconstruct the two proteins in the pair. An experimental analysis was performed on two public PPI data sets containing testing pairs formed using both seen and unseen protein sequences. The results show that our approach surpasses, on the two data sets, multiple machine learning classifiers proposed in the literature. |
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| AbstractList | Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen proteins. Supervised autoencoders, which are neural networks trained to simultaneously predict labels and reconstruct their inputs, have been proved to offer generalization guarantees in the linear case. Moreover, the addition of a reconstruction branch was empirically shown to improve the performance of standard neural networks classifiers on several tasks. In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders. The proposed approach consists of initially training a denoising autoencoder on protein sequences, followed by a supervised training stage in which the model learns to both predict whether two proteins interact and to reconstruct the two proteins in the pair. An experimental analysis was performed on two public PPI data sets containing testing pairs formed using both seen and unseen protein sequences. The results show that our approach surpasses, on the two data sets, multiple machine learning classifiers proposed in the literature. |
| Author | Albu, Alexandra-Ioana |
| Author_xml | – sequence: 1 givenname: Alexandra-Ioana surname: Albu fullname: Albu, Alexandra-Ioana email: alexandra.albu@ubbcluj.ro organization: Department of Computer Science, Babeş-Bolyai University 1, M. Kogalniceanu Street, 400084, Cluj-Napoca, Romania |
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| CitedBy_id | crossref_primary_10_1016_j_compbiomed_2024_109449 crossref_primary_10_1016_j_compbiomed_2022_106526 |
| Cites_doi | 10.1039/C7MB00188F 10.1093/bioinformatics/bth483 10.1021/acs.jcim.7b00028 10.1038/nmeth.1611 10.1093/nar/gkn159 10.1186/s12859-017-1700-2 10.1186/s12859-016-1414-x 10.1093/nar/gkn390 10.3390/e23060643 10.1186/s12859-020-03646-8 10.1073/pnas.0607879104 10.1186/s12859-016-1253-9 10.1016/j.sbi.2022.102328 10.1155/2018/4216813 10.1093/bioinformatics/btz328 10.1038/nmeth.2259 10.1021/pr100618t |
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| Keywords | 68T05 protein-protein interaction prediction supervised autoencoders denoising autoencoders 2000 MSC: 92D20 |
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