Evidence map›Paper›PMID 42918992›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

Modeling Antigen Allergenicity with Bioinformatics Approaches.

Isidro Sobrino, José de la Fuente

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Isidro SobrinoSaBio, Instituto de Investigación en Recursos Cinegéticos, IREC (CSIC, UCLM, JCCM), Ronda de Toledo 12, Ciudad Real, 13005, Spain. isidro.sobrino@uclm.es.
José de la FuenteSaBio, Instituto de Investigación en Recursos Cinegéticos, IREC (CSIC, UCLM, JCCM), Ronda de Toledo 12, Ciudad Real, 13005, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Allergenicity is a multifactorial and context-dependent property that emerges from the interaction between intrinsic protein features and host immune mechanisms. Although regulatory frameworks rely primarily on sequence homology to known allergens, increasing numbers of novel and engineered proteins require more comprehensive computational strategies for preclinical assessment. This chapter describes a modular bioinformatic pipeline for the prediction and characterization of potential protein allergens. The workflow integrates FAO/WHO sequence-based criteria for regulatory compliance, structure-informed homology methods, and machine learning-based classifiers. Proteins identified as potential allergens are subsequently subjected to downstream immunological characterization. This stepwise decision framework enables flexible and biologically informed prescreening of candidate proteins, providing a structured approach prior to experimental validation or systems biology-based analyses.

Indexed as

AllergensAntigensComputational BiologyHypersensitivityAnimalsHumansImmunoinformaticsMachine LearningPrediction AlgorithmsSoftwareAllergensAntigensAllergyBiodataDeep learningImmunoinformaticsMachine learningSystems biology

Identifiers

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.