ArticleBioinformatics (Oxford, England)2018
Learned protein embeddings for machine learning.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 116 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
116 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Generative Models for the Discovery of Antiviral Peptides Targeting Dengue Virus: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Embeddings of clinical codes enable knowledge-grounded AI in medicine.NPJ digital medicine · 2026Article
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- ET-Pfam: ensemble transfer learning for protein family prediction.Bioinformatics (Oxford, England) · 2026Article
- Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences.BMC bioinformatics · 2026Article
- Diversity of FAAL enzymes and prediction of their substrate specificity using FAALPred.Protein science : a publication of the Protein Society · 2026Article
- Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.Communications medicine · 2026Article
- PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery.Database : the journal of biological databases and curation · 2026Article
- From randomness to recognition: modeling the evolution of DNA sequence information during enrichment for binding.Bioinformatics advances · 2026Article
- Machine Learning Approaches for Interpretable Antibody Property Prediction Using Structural Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- GRUATT-AVP: leveraging a novel attention-based gated recurrent unit to advance the accuracy of antiviral peptide prediction.Scientific reports · 2025Article
- A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-DependentACS synthetic biology · 2025Article
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.Nature methods · 2025Article
- Biophysics-based protein language models for protein engineering.bioRxiv : the preprint server for biology · 2025Article
- In vivo electrophysiology recordings and computational modeling can predict octopus arm movement.Bioelectronic medicine · 2025Article
- DeepKinome: quantitative prediction of kinase binding affinity by a compound using deep learning based regression model.Frontiers in molecular biosciences · 2025Article
- A Benchmarking Platform for Assessing Protein Language Models on Function-Related Prediction Tasks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Genome language modeling (GLM): a beginner's cheat sheet.Biology methods & protocols · 2025Article
- IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions.Frontiers in bioinformatics · 2025Article
56 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Motivation: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences with optimal properties. Machine-learning models generally require that their inputs be vectors, and the conversion from a protein sequence to a vector representation affects the model's ability to learn. We propose to learn embedded representations of protein sequences that take advantage of the vast quantity of unmeasured protein sequence data available. These embeddings are low-dimensional and can greatly simplify downstream modeling. Results: The predictive power of Gaussian process models trained using embeddings is comparable to those trained on existing representations, which suggests that embeddings enable accurate predictions despite having orders of magnitude fewer dimensions. Moreover, embeddings are simpler to obtain because they do not require alignments, structural data, or selection of informative amino-acid properties. Visualizing the embedding vectors shows meaningful relationships between the embedded proteins are captured. Availability and implementation: The embedding vectors and code to reproduce the results are available at https://github.com/fhalab/embeddings_reproduction/. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.