ArticleNAR genomics and bioinformatics2022
PANDA2: protein function prediction using graph neural networks.
Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Hierarchical clustering-based coarse-to-fine classification framework for microbial protein function prediction.BMC bioinformatics · 2025Article
- PF-AGCN: an adaptive graph convolutional network for protein-protein interaction-based function prediction.Bioinformatics (Oxford, England) · 2025Article
- GOBeacon: An ensemble model for protein function prediction enhanced by contrastive learning.Protein science : a publication of the Protein Society · 2025Article
- GOBoost: leveraging long-tail gene ontology terms for accurate protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.Briefings in bioinformatics · 2024Article
- PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction.NAR genomics and bioinformatics · 2024Article
- PANDA-3D: protein function prediction based on AlphaFold models.NAR genomics and bioinformatics · 2024Article
- Mutual annotation-based prediction of protein domain functions with Domain2GO.Protein science : a publication of the Protein Society · 2024Article
- A comprehensive review and comparison of existing computational methods for protein function prediction.Briefings in bioinformatics · 2024Review
- Review
- Hierarchical graph transformer with contrastive learning for protein function prediction.Bioinformatics (Oxford, England) · 2023Article
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
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
Abstract
High-throughput sequencing technologies have generated massive protein sequences, but the annotations of protein sequences highly rely on the low-throughput and expensive biological experiments. Therefore, accurate and fast computational alternatives are needed to infer functional knowledge from protein sequences. The gene ontology (GO) directed acyclic graph (DAG) contains the hierarchical relationships between GO terms but is hard to be integrated into machine learning algorithms for functional predictions. We developed a deep learning system named PANDA2 to predict protein functions, which used the cutting-edge graph neural network to model the topology of the GO DAG and integrated the features generated by transformer protein language models. Compared with the top 10 methods in CAFA3, PANDA2 ranked first in cellular component ontology (CCO), tied first in biological process ontology (BPO) but had a higher coverage rate, and second in molecular function ontology (MFO). Compared with other recently-developed cutting-edge predictors DeepGOPlus, GOLabeler, and DeepText2GO, and benchmarked on another independent dataset, PANDA2 ranked first in CCO, first in BPO, and second in MFO. PANDA2 can be freely accessed from http://dna.cs.miami.edu/PANDA2/.
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.