Evidence map›Paper›PMID 41509393›Full record

ArticlebioRxiv : the preprint server for biology2025

Machine Learning Reveals Proteome-Encoded Growth Predictors of

Abraham Osinuga, Mark Kathol, Rajib Saha

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

5 · Who and what money

Authors and funding

3 authors.

Abraham OsinugaDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.ORCID 0000-0002-2587-8704
Mark KatholDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.ORCID 0000-0001-5976-6543
Rajib SahaDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.ORCID 0000-0002-2974-0243

Funding

A Predictive Modeling Framework to Dissect the Dynamic Immunometabolic Responses to Pathogenic infection and the Kinetic Reprogramming of Metabolism in Cancer Cell SystemR35GM143009 · NIGMS · UNIVERSITY OF NEBRASKA LINCOLN · PI SAHA, RAJIB · 2021 to 2025
$1.8M
NIGMS NIH HHS R35 GM143009
6 · The paper itself

Abstract

Microbial utilization of lignin-derived aromatics requires extensive metabolic flexibility, yet growth outcomes vary sharply with substrate chemistry and oxygen availability. Whether this variability reflects distinct growth programs or alternative realizations of shared biochemical constraints remains unclear. Here, we combine quantitative proteomics with cross-condition machine learning to test whether growth-rate variation in

Indexed as

growth predictionslignin breakdown productsmachine learningmetabolic versatilityProteomicsRhodopseudomonas palustris

Identifiers

PMID41509393
PMCPMC12776462

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.