ArticleGenomics, proteomics & bioinformatics2023
NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations.
Article in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- CB-Search: a method for searching for similar protein motifs with biased compositions.BMC bioinformatics · 2026Article
- Inferring Local Protein Structural Similarity from Sequence Alone.Journal of chemical information and modeling · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- Evolutionary profile enhancement improves protein function annotation for remote homologs.bioRxiv : the preprint server for biology · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Ab-initio amino acid sequence design from protein text description with ProtDAT.Nature communications · 2025Article
- Large language models for drug discovery and development.Patterns (New York, N.Y.) · 2025Review
- SPACE: STRING proteins as complementary embeddings.Bioinformatics (Oxford, England) · 2025Article
- PF-AGCN: an adaptive graph convolutional network for protein-protein interaction-based function prediction.Bioinformatics (Oxford, England) · 2025Article
- GOAnnotator: accurate protein function annotation using automatically retrieved literature.Bioinformatics (Oxford, England) · 2025Article
- Unveiling Salt Tolerance Mechanisms in Plants: Integrating the KANMB Machine Learning Model With Metabolomic and Transcriptomic Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- In-Context Learning can distort the relationship between sequence likelihoods and biological fitness.ArXiv · 2025Article
- Structure-informed protein language models are robust predictors for variant effects.Human genetics · 2025Article
- Learning maximally spanning representations improves protein function annotation.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins. Recently, protein language models have been proposed to learn informative representations [e.g., Evolutionary Scale Modeling (ESM)-1b embedding] from protein sequences based on self-supervision. Here, we represented each protein by ESM-1b and used logistic regression (LR) to train a new model, LR-ESM, for AFP. The experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO 2.0. Therefore, by incorporating LR-ESM into NetGO 2.0, we developed NetGO 3.0 to improve the performance of AFP extensively. NetGO 3.0 is freely accessible at https://dmiip.sjtu.edu.cn/ng3.0.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.