Evidence map›Paper›PMID 42282608›Full record

ArticlebioRxiv : the preprint server for biology2026

CLASPP: A unified model for predicting post-translational modifications.

Nathan Gravel, Zhongliang Zhou, Ruili Fang, Austin Downes, Saber Soleymani, Natarajan Kannan

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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

5 · Who and what money

Authors and funding

6 authors.

Nathan GravelInstitute of Bioinformatics, University of Georgia, GA 30602, USA.
Zhongliang ZhouSchool of Computing, University of Georgia, GA 30602, USA.
Ruili FangSchool of Computing, University of Georgia, GA 30602, USA.
Austin DownesSchool of Computing, University of Georgia, GA 30602, USA.
Saber SoleymaniInstitute for Insight, Georgia State University, Atlanta, GA 30303, USA.
Natarajan KannanInstitute of Bioinformatics, University of Georgia, GA 30602, USA.

Funding

Unlocking sequence-structure-function-disease relationships in large protein super-familiesR35GM139656 · NIGMS · UNIVERSITY OF GEORGIA · PI KANNAN, NATARAJAN · 2021 to 2025
$2.2M
NIGMS NIH HHS R35 GM139656
6 · The paper itself

Abstract

Post-Translational Modifications (PTMs) are a fundamental mechanism for regulating cellular pathways and increasing the functional diversity of the proteome. Accurately predicting the PTM types that are likely to occur at a given site in the primary sequence is a key challenge in functional proteomics. Existing PTM prediction models predominantly focus on either single PTM types or employ ensemble methods that combine multiple models to predict different PTM types. This fragmentation is largely driven by the vast imbalance in data availability across PTM types, making it difficult to predict multiple PTM types with a single model. To address this limitation, we present the

Identifiers

PMID42282608
PMCPMC13252087

What OpenQuestion holds

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LicenceCC BY
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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.