ReviewComputational and structural biotechnology journal2021
The language of proteins: NLP, machine learning & protein sequences.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 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
150 citing papers in PubMed, 1 synthesis or guideline pooled it, 374 citations in OpenAlex.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Sequence-based artificial intelligence-guided inhibitory peptides to counteract AAV neutralizing antibodies in gene therapy.Molecular therapy. Advances · 2026Article
- Bridging linguistic reasoning and biophysical reality toward peptide engineering via instruction-tuned language modeling.Bioinformatics (Oxford, England) · 2026Article
- Literature-informed gene extraction and ranking for multimodal data fusion.Briefings in bioinformatics · 2026Article
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- Optimizing protein tokenization: reduced amino acid alphabets for efficient and accurate protein language models.Bioinformatics (Oxford, England) · 2026Article
- Molecular Adaptations of Thermophilic Proteins-From Mechanistic Understanding to Machine Learning Approaches.ChemistryOpen · 2026Review
- Target sequence-conditioned design of peptide binders using masked language modeling.Nature biotechnology · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Protein science : a publication of the Protein Society · 2026Article
- Compressing the collective knowledge of ESM into a single protein language model.Nature methods · 2026Article
- Amino acid composition drives aggregation during peptide synthesis.Nature chemistry · 2026Article
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- InterFeat: a pipeline for finding interesting scientific features.Scientific reports · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Controllable protein design via autoregressive direct coupling analysis conditioned on principal components.PLoS computational biology · 2026Article
- BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects across Class A GPCRs.Journal of chemical information and modeling · 2026Article
- Deep generative modeling captures maturation-dependent pairing patterns in human antibodies.iScience · 2026Article
- SPPIPred: Stacking-based ensemble learning model for identification of protein-protein interaction.PloS one · 2026Article
90 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
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural language processing (NLP) is a field of computer science concerned with automated text and language analysis. In recent years, following a series of breakthroughs in deep and machine learning, NLP methods have shown overwhelming progress. Here, we review the success, promise and pitfalls of applying NLP algorithms to the study of proteins. Proteins, which can be represented as strings of amino-acid letters, are a natural fit to many NLP methods. We explore the conceptual similarities and differences between proteins and language, and review a range of protein-related tasks amenable to machine learning. We present methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models. In particular, we focus on recent innovations such as masked language modeling, self-supervised learning and attention-based models. Finally, we discuss trends and challenges in the intersection of NLP and protein research.
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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.