Evidence map›Paper›PMID 40161693›Full record

ArticlebioRxiv : the preprint server for biology2025

AUC-PR is a More Informative Metric for Assessing the Biological Relevance of In Silico Cellular Perturbation Prediction Models.

Hongxu Zhu, Amir Asiaee, Leila Azinfar, Jun Li, Han Liang, Ehsan Irajizad, Kim-Anh Do, James P Long

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

8 authors.

Hongxu ZhuDepartment of Biostatistics and Data Science, The University of Texas Health Science Center at Houston School of Public Health, 1200 Pressler St., 77030, Texas, USA.ORCID 0000-0003-3774-4495
Amir AsiaeeDepartment of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, 37203, Tennessee, USA.
Leila AzinfarDepartment of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, 37203, Tennessee, USA.
Jun LiDepartment of Bioinfomatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 7007 Bertner Ave., 77030, Texas, USA.
Han LiangDepartment of Bioinfomatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 7007 Bertner Ave., 77030, Texas, USA.
Ehsan IrajizadDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Ave., 77030, Texas, USA.
Kim-Anh DoDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Ave., 77030, Texas, USA.
James P LongDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Ave., 77030, Texas, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
Center for Clinical and Translational SciencesUM1TR004906 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Maria Eulalia Fernandez, LORNA H. MCNEILL · 2024 to 2026
$16.7M
Causal Effect Estimation of Regulatory MoleculesR00HG011367 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI ASIAEETAHERI, AMIR · 2021 to 2023
$734k
NCATS NIH HHS UM1 TR004906NCI NIH HHS P30 CA016672NHGRI NIH HHS R00 HG011367
6 · The paper itself

Abstract

In silico perturbation models, computational methods which can predict cellular responses to perturbations, present an opportunity to reduce the need for costly and time-intensive in vitro experiments. Many recently proposed models predict high-dimensional cellular responses, such as gene or protein expression to perturbations such as gene knockout or drugs. However, evaluating in silico performance has largely relied on metrics such as

Indexed as

Cellular perturbation experimentsDifferentially expressed genesEvaluation metricsIn silico models

Identifiers

PMID40161693
PMCPMC11952326

What OpenQuestion holds

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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.