ArticleeLife2023
ProteInfer, deep neural networks for protein functional inference.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
- InferiorMicrobial genomics · 2024Pooled it
- Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes.PLoS computational biology · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Large language models enhance annotation of enzymes in metagenomes.Science advances · 2026Article
- Property guidance for protein sequence generative models with ProteinGuide.Nature biotechnology · 2026Article
- WASP: a pipeline for functional annotation prediction based on AlphaFold structural models.Nature communications · 2026Article
- EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework.Patterns (New York, N.Y.) · 2026Article
- DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment.Briefings in bioinformatics · 2026Article
- How not to be seen: predicting unseen enzyme functions using contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- Interpretable convolutional neural networks for sequence-based classification and discovery of plastic-degrading enzymes.Applied and environmental microbiology · 2026Article
- MolluscaGenes: A Transcriptomic Database for the Mollusca.bioRxiv : the preprint server for biology · 2026Article
- Gap Analysis of Metabolic Conversions of Off-Flavors and Antinutrients in Plant-Based Substrates.Comprehensive reviews in food science and food safety · 2026Review
- STAR-GO: improving protein function prediction by learning to hierarchically integrate ontology-informed semantic embeddings.Bioinformatics (Oxford, England) · 2026Article
- Scalable homology detection with ERAST.Nature biotechnology · 2026Article
- Evolutionary profile enhancement improves protein function annotation for remote homologs.bioRxiv : the preprint server for biology · 2026Article
- How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning.bioRxiv : the preprint server for biology · 2026Article
- Molecular basis for anti-jumbo phage immunity by AVAST type 5.Molecular cell · 2026Article
- Distinct prophage infections in colorectal cancer-associated Bacteroides fragilis.Communications medicine · 2026Article
- CACLENS: A Multitask Deep Learning System for Enzyme Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we introduce ProteInfer, which instead employs deep convolutional neural networks to directly predict a variety of protein functions - Enzyme Commission (EC) numbers and Gene Ontology (GO) terms - directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight software interfaces, which we demonstrate with an in-browser graphical interface for protein function prediction in which all computation is performed on the user's personal computer with no data uploaded to remote servers. Moreover, these models place full-length amino acid sequences into a generalised functional space, facilitating downstream analysis and interpretation. To read the interactive version of this paper, please visit https://google-research.github.io/proteinfer/.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.