Evidence map›Paper›PMID 42779711›Full record

ArticlebioRxiv : the preprint server for biology2026

KSTAR v1.2: A faster and more and accessible KSTAR for kinase activity inference.

Sam Crowl, Joseph-Levi Custer, Gabriela Salazar Lopez, Candace Lei-Dadey, Adrian A Shimpi, Kristen M Naegle

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

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

6 authors.

Sam CrowlBiomedical Engineering, University of Virginia, Charlottesville, 22903, Virginia, USA.
Joseph-Levi CusterBiomedical Engineering, University of Virginia, Charlottesville, 22903, Virginia, USA.
Gabriela Salazar LopezBiomedical Engineering, University of Virginia, Charlottesville, 22903, Virginia, USA.
Candace Lei-DadeyBiomedical Engineering, University of Virginia, Charlottesville, 22903, Virginia, USA.
Adrian A ShimpiBiomedical Engineering, University of Virginia, Charlottesville, 22903, Virginia, USA.ORCID 0000-0002-0782-0496
Kristen M NaegleBiomedical Engineering, University of Virginia, Charlottesville, 22903, Virginia, USA.ORCID 0000-0001-7146-9592

Funding

Inferring Kinase Activity from Tumor Phosphoproteomic DataU01CA284193 · NCI · UNIVERSITY OF VIRGINIA · PI NAEGLE, KRISTEN M · 2023 to 2025
$961k
NCI NIH HHS U01 CA284193
6 · The paper itself

Abstract

Motivation: KSTAR is an algorithm with high flexibility for inferring kinase activity from any phosphoproteomic pipeline. However, in its first instantiation (v0.1) it requires Python programming and lots of memory and computational resources. Hence, we wished to improve speed and accessibility for broader uptake by researchers. Results: Here, we provide an updated algorithm that improves speed and memory, without affecting accuracy, along with some new features for increased usability and insight. KSTAR v1.2 has also been integrated into Galaxy for programming-free activity analysis and ProteomeScout for dataset preparation and interactive plotting. Availability and implementation:

Indexed as

databasepost-translational modificationsproteins

Identifiers

PMID42779711
PMCPMC13596325

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.