Evidence map›Paper›PMID 40106150›Full record

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

Deep Learning-Driven Computational Approaches for Studying Intrinsically Disordered Regions in S100-A9.

Gionathan L Distefano, Fabio D'Amico

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.

Gionathan L DistefanoDepartment of Mathematics and Computer Science, University of Catania, Catania, Italy.
Fabio D'AmicoDepartment of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy. f.damico@unict.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intrinsically disordered regions (IDRs) are flexible protein regions and lack a fixed three-dimensional structure, which makes them difficult to study using traditional structural methods. However, artificial intelligence can be helpful in predicting, analyzing, and modeling these regions. This chapter provides a simple protocol for a preliminary-level approach to identifying protein IDRs. By reporting on the S100-A9 protein as a case study example, characterization of the IDRs of this molecule could provide further details on the complex molecular interactions involved in psoriasis, particularly those related to inflammation, immune dysregulation, and keratinocyte behavior.

Indexed as

Calgranulin BComputational BiologyDeep LearningIntrinsically Disordered ProteinsHumansModels, MolecularProtein ConformationCalgranulin BIntrinsically Disordered ProteinsArtificial IntelligenceDeep LearningIntrinsically Disordered Regions (IDRs)S100-A9

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.