ArticleFrontiers in bioengineering and biotechnology2020
SDN2GO: An Integrated Deep Learning Model for Protein Function Prediction.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- HPOseq: a deep ensemble model for predicting the protein-phenotype relationships based on protein sequences.BMC bioinformatics · 2025Article
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- Deep Learning Enhanced Label-Free Action Potential Detection Using Plasmonic-Based Electrochemical Impedance Microscopy.Analytical chemistry · 2024Article
- A comprehensive review and comparison of existing computational methods for protein function prediction.Briefings in bioinformatics · 2024Review
- Improvements in viral gene annotation using large language models and soft alignments.BMC bioinformatics · 2024Article
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
- Review
- HostNet: improved sequence representation in deep neural networks for virus-host prediction.BMC bioinformatics · 2023Article
- HNetGO: protein function prediction via heterogeneous network transformer.Briefings in bioinformatics · 2023Article
- Organizing the bacterial annotation space with amino acid sequence embeddings.BMC bioinformatics · 2022Article
- Accurate protein function prediction via graph attention networks with predicted structure information.Briefings in bioinformatics · 2022Article
- DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals.Frontiers in plant science · 2022Article
- Article
- Review
- CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network.BMC bioinformatics · 2021Article
- Improving gene function predictions using independent transcriptional components.Nature communications · 2021Article
- Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy.Frontiers in bioinformatics · 2021Article
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3 authors.
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Abstract
The assignment of function to proteins at a large scale is essential for understanding the molecular mechanism of life. However, only a very small percentage of the more than 179 million proteins in UniProtKB have Gene Ontology (GO) annotations supported by experimental evidence. In this paper, we proposed an integrated deep-learning-based classification model, named SDN2GO, to predict protein functions. SDN2GO applies convolutional neural networks to learn and extract features from sequences, protein domains, and known PPI networks, and then utilizes a weight classifier to integrate these features and achieve accurate predictions of GO terms. We constructed the training set and the independent test set according to the time-delayed principle of the Critical Assessment of Function Annotation (CAFA) and compared it with two highly competitive methods and the classic BLAST method on the independent test set. The results show that our method outperforms others on each sub-ontology of GO. We also investigated the performance of using protein domain information. We learned from the Natural Language Processing (NLP) to process domain information and pre-trained a deep learning sub-model to extract the comprehensive features of domains. The experimental results demonstrate that the domain features we obtained are much improved the performance of our model. Our deep learning models together with the data pre-processing scripts are publicly available as an open source software at https://github.com/Charrick/SDN2GO.
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