Evidence map›Paper›PMID 35387383›Full record

ArticleNAR genomics and bioinformatics2022

OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.

Jezreel Pantaleón García, Vikram V Kulkarni, Tanner C Reese, Shradha Wali, Saima J Wase, Jiexin Zhang, Ratnakar Singh, Mauricio S Caetano, Humam Kadara, Seyed Javad Moghaddam and 4 more

Open access · goldAbstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.4field-weighted citation impact, top 10% of its field
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

13 citing papers in PubMed, 17 citations in OpenAlex.

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  13. Minimizing caging effects in murine lung microbiome studies.American journal of physiology. Lung cellular and molecular physiology · 2022
    Article
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

14 authors at 1 institution in 1 country.

Jezreel Pantaleón GarcíaDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.ORCID https://orcid.org/0000-0002-9647-2584
Vikram V KulkarniDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.
Tanner C ReeseDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.
Shradha WaliDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.
Saima J WaseDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.
Jiexin ZhangDepartment of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Ratnakar SinghDepartment of Thoracic, Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Mauricio S CaetanoDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.
Humam KadaraDepartment of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Seyed Javad MoghaddamDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.ORCID https://orcid.org/0000-0002-5350-307X
Faye M JohnsonDepartment of Thoracic, Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jing WangDepartment of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Yongxing WangDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.
Scott E EvansDepartment of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA.
The University of Texas MD Anderson Cancer Center · US

Funding

Inducible epithelial resistance: a program investigating mechanisms to protect against acute and chronic complications of pneumoniaR35HL144805 · NHLBI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI EVANS, SCOTT E. · 2019 to 2025
$6.2M
Inducible epithelial antiviral resistance to prevent asthmaDP2HL123229 · NHLBI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI EVANS, SCOTT E. · 2013 to 2013
$2.4M
Epithelial mechanisms of inducible resistance to AML-associated pneumoniaR01HL117976 · NHLBI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI EVANS, SCOTT E. · 2014 to 2018
$2.0M
Immunotherapeutic Modalities for K-ras Mutant Lung Cancer: Sex- and Cell Type-Specific Roles of IL-6/STAT3 signalingR01CA225977 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MOGHADDAM, SEYED JAVAD MIRHASSANI · 2018 to 2022
$1.8M
NCI NIH HHS R01 CA225977NHLBI NIH HHS DP2 HL123229NHLBI NIH HHS R01 HL117976NHLBI NIH HHS R35 HL144805
6 · The paper itself

Abstract

Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug-drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.

Identifiers

PMID35387383
PMCPMC8982434
OpenAlexW4225650434

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.