ReviewGenes2020
Automatic Gene Function Prediction in the 2020's.
Review in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed.
- Go Big or go home: a new gene ontology subset that improves plant gene function prediction.Plant methods · 2026Article
- Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations.PloS one · 2026Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- NetGO 3.0: A Recent Protein Function Prediction Tool Based on Protein Language Model.Methods in molecular biology (Clifton, N.J.) · 2025Article
- GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.Bioinformatics (Oxford, England) · 2024Article
- Machine learning-aided design and screening of an emergent protein function in synthetic cells.Nature communications · 2024Article
- Review
- SynBioTools: a one-stop facility for searching and selecting synthetic biology tools.BMC bioinformatics · 2023Article
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- An informatic workflow for the enhanced annotation of excretory/secretory proteins ofComputational and structural biotechnology journal · 2023Article
- Hierarchical deep learning for predicting GO annotations by integrating protein knowledge.Bioinformatics (Oxford, England) · 2022Article
- Activity-based annotation: the emergence of systems biochemistry.Trends in biochemical sciences · 2022Review
- CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation.PLoS computational biology · 2022Article
- Functional characterization of prokaryotic dark matter: the road so far and what lies ahead.Current research in microbial sciences · 2022Review
- PANNZER-A practical tool for protein function prediction.Protein science : a publication of the Protein Society · 2022Article
- RNABMC genomics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.
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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.