ArticlebioRxiv : the preprint server for biology2024
Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.
Ethan Bustad, Edson Petry, Oliver Gu, Braden T Griebel, Tige R Rustad, David R Sherman, Jason H Yang, Shuyi Ma
Abstract readPreprint
In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
8 authors.
Ethan BustadCenter for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.ORCID 0000-0002-0230-2913 Edson PetryCenter for Emerging and Re-emerging Pathogens, Rutgers New Jersey Medical School, Newark NJ, USA.
Oliver GuCenter for Emerging and Re-emerging Pathogens, Rutgers New Jersey Medical School, Newark NJ, USA.
Braden T GriebelCenter for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.ORCID 0009-0003-3617-5734 Tige R RustadCenter for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.ORCID 0000-0003-2996-2943 David R ShermanDepartment of Microbiology, University of Washington, Seattle WA, USA.
Jason H YangCenter for Emerging and Re-emerging Pathogens, Rutgers New Jersey Medical School, Newark NJ, USA.ORCID 0000-0003-0921-4657 Shuyi MaCenter for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.ORCID 0000-0003-0783-1328 Funding
Technology CoreU19AI135976 · NIAID · SEATTLE CHILDREN'S HOSPITAL · PI SHERMAN, DAVID R · 2018 to 2022
$17.0MSystems Biology CoreU19AI162598 · NIAID · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI ALLAND, DAVID, ELLNER, JERROLD J. · 2021 to 2025
$13.9MSeattle Tuberculosis Research Advancement CenterP30AI168034 · NIAID · UNIVERSITY OF WASHINGTON · PI Rhea N Coler, CHETAN SESHADRI · 2022 to 2026
$6.3MGenetic, Metabolic and Regulatory Control of MIC and Relapse in M. tuberculosisR01AI146194 · NIAID · UNIVERSITY OF WASHINGTON · PI ALLAND, DAVID, SHERMAN, DAVID R · 2020 to 2024
$4.1MA multifactorial pipeline to dissect combinatorial drug efficacy in TuberculosisR01AI150826 · NIAID · UNIVERSITY OF WASHINGTON · PI SHERMAN, DAVID R · 2021 to 2024
$2.9MNetwork Dissection of Host-Pathogen Interactions in Mycobacterium tuberculosis InfectionDP2AI164249 · NIAID · SEATTLE CHILDREN'S HOSPITAL · PI MA, SHUYI · 2021 to 2025
$2.8MEffects of Host Metabolic Variation on Antibiotic SusceptibilityR00GM118907 · NIGMS · RBHS-NEW JERSEY MEDICAL SCHOOL · PI YANG, JASON HUNG-YING · 2020 to 2022
$747kNIAID NIH HHS DP2 AI164249NIAID NIH HHS P30 AI168034NIAID NIH HHS R01 AI146194NIAID NIH HHS R01 AI150826NIAID NIH HHS U19 AI135976NIAID NIH HHS U19 AI162598NIGMS NIH HHS R00 GM118907
6 · The paper itselfAbstract
PubMed holds no abstract for this paper.
Indexed as
growth regulationinterpretable machine learningMycobacterium tuberculosisnetwork modelingstress adaptationtranscriptional regulationtranscriptional regulatory network inference
Identifiers
PMID39386570
PMCPMC11463588
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