Evidence map›Paper›PMID 26515819›Full record

ArticleBioinformatics (Oxford, England)2016

Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

Massimo Andreatta, Morten Nielsen

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 654 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
654citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

654 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Ensembles ofbioRxiv : the preprint server for biology · 2026
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  11. Automating neoantigen selection for personalized cancer vaccine design.medRxiv : the preprint server for health sciences · 2026
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594 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Massimo AndreattaInstituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina and.
Morten NielsenInstituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina and Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark.

Funding

PHS HHS HHSN272201200010C
6 · The paper itself

Abstract

motivationMany biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.

resultsWe show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. AVAILABILITY AND IMPLEMENTATION: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at: http://www.cbs.dtu.dk/services/NetMHC-4.0.

Indexed as

AlgorithmsNeural Networks, ComputerComputational BiologyEpitopesHistocompatibility Antigens Class IHumansPeptide FragmentsProtein BindingSequence AlignmentEpitopesHistocompatibility Antigens Class IPeptide Fragments

Identifiers

PMID26515819
PMCPMC6402319

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.