ArticleJournal of immunological methods2011
Dana-Farber repository for machine learning in immunology.
Article in Journal of immunological methods, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses 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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy.Genome biology · 2015Pooled it
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Lynch syndrome cancer vaccines: A roadmap for the development of precision immunoprevention strategies.Frontiers in oncology · 2023Review
- A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach.Frontiers in immunology · 2021Article
- A novel neoantigen discovery approach based on chromatin high order conformation.BMC medical genomics · 2020Article
- Review
- Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes.PLoS computational biology · 2018Article
- Breast cancer vaccination comes to age: impacts of bioinformatics.BioImpacts : BI · 2018Review
- Computational genomics tools for dissecting tumour-immune cell interactions.Nature reviews. Genetics · 2016Review
- Immunoinformatics and epitope prediction in the age of genomic medicine.Genome medicine · 2015Review
- PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity.BMC bioinformatics · 2013Article
- Cancer vaccines: state of the art of the computational modeling approaches.BioMed research international · 2013Review
- Bioinformatics for cancer immunology and immunotherapy.Cancer immunology, immunotherapy : CII · 2012Review
- The utility and limitations of current Web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection.Journal of immunology (Baltimore, Md. : 1950) · 2012Article
- TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules.PloS one · 2012Article
- InCoB2012 Conference: from biological data to knowledge to technological breakthroughs.BMC bioinformatics · 2012Article
- Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.Computational and mathematical methods in medicine · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.
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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.