ArticleAdvances in neural information processing systems2019
Evaluating Protein Transfer Learning with TAPE.
Article in Advances in neural information processing systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 384 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
384 citing papers in PubMed, 3 syntheses or guidelines 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
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Sequence-based artificial intelligence-guided inhibitory peptides to counteract AAV neutralizing antibodies in gene therapy.Molecular therapy. Advances · 2026Article
- Multidimensional maturation of antibody variable domains with machine-learning assistance.mAbs · 2026Article
- Article
- DeepSAP: an integrated generative and predictive deep-learning framework for controllable design of self-assembling peptides.Bioinformatics (Oxford, England) · 2026Article
- Generating protein hydrogels with customizable stress relaxation behavior via deep learning-driven entanglement design.Nature communications · 2026Article
- Review
- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Phys-AbGAT: a physics-informed multi-task graph attention network for robust antibody-antigen binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- Article
- Peptide language pragmatic analysis and two-stage hierarchical learning framework for therapeutic peptide prediction.BMC biology · 2026Article
- The limits of bio-molecular modeling with large language models: a cross-scale evaluation.Bioinformatics (Oxford, England) · 2026Article
- AbAgKer: a unified semi-supervised framework for antigen-antibody binding affinity and kinetics prediction.Bioinformatics (Oxford, England) · 2026Article
- PeptiVerse: A unified platform for therapeutic peptide property prediction.Nature communications · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
324 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
8 authors.
Funding
Abstract
Machine learning applied to protein sequences is an increasingly popular area of research. Semi-supervised learning for proteins has emerged as an important paradigm due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques. To facilitate progress in this field, we introduce the Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. We curate tasks into specific training, validation, and test splits to ensure that each task tests biologically relevant generalization that transfers to real-life scenarios. We benchmark a range of approaches to semi-supervised protein representation learning, which span recent work as well as canonical sequence learning techniques. We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases. Despite this increase, in several cases features learned by self-supervised pretraining still lag behind features extracted by state-of-the-art non-neural techniques. This gap in performance suggests a huge opportunity for innovative architecture design and improved modeling paradigms that better capture the signal in biological sequences. TAPE will help the machine learning community focus effort on scientifically relevant problems. Toward this end, all data and code used to run these experiments are available at https://github.com/songlab-cal/tape.
Identifiers
33390682PMC7774645What 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.