ReviewBioinformatics and biology insights2015
Machine Learning Methods for Predicting HLA-Peptide Binding Activity.
Review in Bioinformatics and biology insights, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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
35 citing papers in PubMed.
- Computational identification of antigens and development of a multi-epitope vaccine for bacterial vaginosis caused by Gardnerella vaginalis.Amino acids · 2026Article
- Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review).International journal of oncology · 2025Review
- Interpretable machine learning models for predicting the antitumor effects of metal and metal oxide nanomaterials.RSC advances · 2025Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- SiaScoreNet: a siamese neural network-based model integrating prediction scores for HLA-peptide interaction prediction.Bioinformatics advances · 2025Article
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies.Vaccines · 2024Article
- Machine learning and deep learning for brain tumor MRI image segmentation.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials.Nanomaterials (Basel, Switzerland) · 2022Article
- SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development.Frontiers in immunology · 2022Article
- Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS).Methods in molecular biology (Clifton, N.J.) · 2022Article
- The pockets guide to HLA class I molecules.Biochemical Society transactions · 2021Review
- Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.Journal of chemical theory and computation · 2021Article
- DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information.BMC bioinformatics · 2021Article
- AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides.Scientific reports · 2020Article
- Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions.Journal of immunology (Baltimore, Md. : 1950) · 2020Article
- Rapid microsphere-assisted peptide screening (MAPS) of promiscuous MHCII-binding peptides in Zika virus envelope protein.AIChE journal. American Institute of Chemical Engineers · 2020Article
- Identification and ranking of recurrent neo-epitopes in cancer.BMC medical genomics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis.
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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.