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Predicting fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

Frontiers in Tuberculosis(2025)10.3389/ftubr.2025.1500899Source: DataRank Database

Predicting fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning is a research paper published in Frontiers in Tuberculosis (2025). On theSindex it has a DataRank of 0.295. It has been cited 5 times, with 4 citing works in its 1-hop citation network.

N/A

0.295DataRank · unranked

Scored on demand from live citation data

Open Access5 citations · 4 citing works in its network

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Linked data & code

geo doi

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (15 grants)Impact (FWCI 3.77)OA: goldTopics (26)IDs (PubMed, PMC, OpenAlex)Annotations (2)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 91%Citation Network 9%

Base Score Contribution

0.269

From this paper's citation signal

Citation Network Contribution

0.0262

From 1 citing papers with measurable signal

Learn more about DataRank methodology →

Top 4 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 4 citers.

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ACS Synthetic Biology20260 citations104 referencesContributes 0.000
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Journal of Studies in Science and Engineering20260 citations34 referencesContributes 0.000

Authors (8)

Edson Petry,Oliver Gu,Braden T Griebel ORCID,Tige R. Rustad ORCID,David R. Sherman ORCID

Live enrichment

PubMed:40678166 PMC:PMC12269550 OpenAlex:https://openalex.org/W4409074589

National Institutes of Health

Grant: DP2AI164249

National Institutes of Health

Grant: R00GM118907

National Institutes of Health

Grant: R01AI146194

National Institutes of Health

Grant: R01AI150826

National Institutes of Health

Grant: U19AI11276

National Institutes of Health

Grant: U19AI135976

National Institutes of Health

Grant: U19AI62598

Agilent Technologies

Grant: Agilent Early Career Professor Award

NIAID NIH HHS

Grant: U19 AI162598

National Institutes of Health

Grant: 5DP2AI164249-02

Network Dissection of Host-Pathogen Interactions in Mycobacterium tuberculosis Infection

National Institutes of Health

Grant: 5U19AI162598-02

Bacterial and Host Heterogeneity in TB latency, persistence and progression

National Institutes of Health

Grant: 5R00GM118907-04

Effects of Host Metabolic Variation on Antibiotic Susceptibility

National Institutes of Health

Grant: 1U19AI135976-01

Omics for TB: Response to Infection and Treatment

National Institutes of Health

Grant: 5R01AI150826-02

A multifactorial pipeline to dissect combinatorial drug efficacy in Tuberculosis

National Institutes of Health

Grant: 5R01AI146194-02

Genetic, Metabolic and Regulatory Control of MIC and Relapse in M. tuberculosis

FWCI

3.77

Citation Percentile

0.9%

Citation Trend

2025

2026

goldLicense: cc-by

journalhttps://doi.org/10.3389/ftubr.2025.1500899

publisherhttps://doi.org/10.3389/ftubr.2025.1500899

publisherhttps://www.frontiersin.org/articles/10.3389/ftubr.2025.1500899/full

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40678166

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12269550

Fields of Study

Tuberculosis Research and EpidemiologyBacterial Genetics and BiotechnologyMachine Learning in Bioinformatics

Keywords

Mycobacterium tuberculosisComputational biologyBiologyTuberculosisGene regulatory networkDiseaseTranscriptomeTranscription factorBioinformaticsMachine learningGene expressionGeneMedicineGeneticsComputer scienceHypoxiaTranscriptional regulationGrowth regulationStress AdaptationNetwork ModelingNetwork InferenceInterpretable Machine LearningArticle

Sustainable Development Goals

SDG 0: Good health and well-being

Predicting fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

FAIR Checklist

DataRank Breakdown

Top 4 citers driving the network score

Authors (8)

Funding (15 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)