ArticleMolecules (Basel, Switzerland)2017
ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment.Briefings in bioinformatics · 2026Article
- A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins.Journal of chemical information and modeling · 2025Article
- A molecular representation system with a common reference frame for analyzing triterpenoid structural diversity.Plant communications · 2025Article
- Learning maximally spanning representations improves protein function annotation.bioRxiv : the preprint server for biology · 2025Article
- Lipid Trafficking in Diverse Bacteria.Accounts of chemical research · 2025Review
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- PANDA-3D: protein function prediction based on AlphaFold models.NAR genomics and bioinformatics · 2024Article
- ProtAgents: protein discoveryDigital discovery · 2024Article
- NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning.BMC genomics · 2024Article
- Optimized model architectures for deep learning on genomic data.Communications biology · 2024Article
- Review
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Article
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings.Frontiers in medicine · 2023Article
- Computational identification of promoters inFrontiers in microbiology · 2023Article
- SANA: cross-species prediction of Gene Ontology GO annotations via topological network alignment.NPJ systems biology and applications · 2022Article
11 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.
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