ArticleBiomolecules2022
GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 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
- LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in protein-based detection and inhibition of AMR pathways.Journal of computer-aided molecular design · 2025Review
- Prediction of circRNA-Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor.Biomolecules · 2025Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- FAPM: functional annotation of proteins using multimodal models beyond structural modeling.Bioinformatics (Oxford, England) · 2024Article
- PANDA-3D: protein function prediction based on AlphaFold models.NAR genomics and bioinformatics · 2024Article
- On knowing a gene: A distributional hypothesis of gene function.Cell systems · 2024Review
- Identification of novel candidate genes associated with non-syndromic tooth agenesis in Mongolian families.Clinical oral investigations · 2023Article
Corrections and comments
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2 authors.
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Abstract
Protein Language Models (PLMs) are shown to be capable of learning sequence representations useful for various prediction tasks, from subcellular localization, evolutionary relationships, family membership, and more. They have yet to be demonstrated useful for protein function prediction. In particular, the problem of automatic annotation of proteins under the Gene Ontology (GO) framework remains open. This paper makes two key contributions. It debuts a novel method that leverages the transformer architecture in two ways. A sequence transformer encodes protein sequences in a task-agnostic feature space. A graph transformer learns a representation of GO terms while respecting their hierarchical relationships. The learned sequence and GO terms representations are combined and utilized for multi-label classification, with the labels corresponding to GO terms. The method is shown superior over recent representative GO prediction methods. The second major contribution in this paper is a deep investigation of different ways of constructing training and testing datasets. The paper shows that existing approaches under- or over-estimate the generalization power of a model. A novel approach is proposed to address these issues, resulting in a new benchmark dataset to rigorously evaluate and compare methods and advance the state-of-the-art.
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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.