ArticleBioinformatics (Oxford, England)2021
TALE: Transformer-based protein function Annotation with joint sequence-Label Embedding.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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72 citing papers in PubMed, 151 citations in OpenAlex.
- 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
- DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment.Briefings in bioinformatics · 2026Article
- Optimizing protein tokenization: reduced amino acid alphabets for efficient and accurate protein language models.Bioinformatics (Oxford, England) · 2026Article
- A unified multimodal model for generalizable zero-shot and supervised protein function prediction.Bioinformatics (Oxford, England) · 2026Article
- Out-of-distribution generalization enhances protein function annotation for low-homology sequences.Briefings in bioinformatics · 2026Article
- Evaluating completeness, coherence, and consistency of genome-scale function annotations.Briefings in bioinformatics · 2026Review
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- STAR-GO: improving protein function prediction by learning to hierarchically integrate ontology-informed semantic embeddings.Bioinformatics (Oxford, England) · 2026Article
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- S2DA-GO: enhancing protein function prediction via gradient-decoupled cross-attention and semantic priors.Frontiers in genetics · 2026Article
- Hierarchical clustering-based coarse-to-fine classification framework for microbial protein function prediction.BMC bioinformatics · 2025Article
- ResNet-Powered Multi-Class Identification of Sequence Patterns for Genome Replication Timing Analysis.Interdisciplinary sciences, computational life sciences · 2025Article
- A scalable equivariant graph network framework for precise protein function prediction.Genome biology · 2025Article
- Application of Protein Structure Encodings and Sequence Embeddings for Transporter Substrate Prediction.Molecules (Basel, Switzerland) · 2025Article
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
- GOAnnotator: accurate protein function annotation using automatically retrieved literature.Bioinformatics (Oxford, England) · 2025Article
- Multistage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- POSA-GO: Fusion of Hierarchical Gene Ontology and Protein Language Models for Protein Function Prediction.International journal of molecular sciences · 2025Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationFacing the increasing gap between high-throughput sequence data and limited functional insights, computational protein function annotation provides a high-throughput alternative to experimental approaches. However, current methods can have limited applicability while relying on protein data besides sequences, or lack generalizability to novel sequences, species and functions.
resultsTo overcome aforementioned barriers in applicability and generalizability, we propose a novel deep learning model using only sequence information for proteins, named Transformer-based protein function Annotation through joint sequence-Label Embedding (TALE). For generalizability to novel sequences we use self-attention-based transformers to capture global patterns in sequences. For generalizability to unseen or rarely seen functions (tail labels), we embed protein function labels (hierarchical GO terms on directed graphs) together with inputs/features (1D sequences) in a joint latent space. Combining TALE and a sequence similarity-based method, TALE+ outperformed competing methods when only sequence input is available. It even outperformed a state-of-the-art method using network information besides sequence, in two of the three gene ontologies. Furthermore, TALE and TALE+ showed superior generalizability to proteins of low similarity, new species, or rarely annotated functions compared to training data, revealing deep insights into the protein sequence-function relationship. Ablation studies elucidated contributions of algorithmic components toward the accuracy and the generalizability; and a GO term-centric analysis was also provided. AVAILABILITY AND IMPLEMENTATION: The data, source codes and models are available at https://github.com/Shen-Lab/TALE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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