Evidence map›Paper›PMID 32142370›Full record

ArticleJCO clinical cancer informatics2020

OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data.

Hayley M Dingerdissen, Frederic Bastian, K Vijay-Shanker, Marc Robinson-Rechavi, Amanda Bell, Nikhita Gogate, Samir Gupta, Evan Holmes, Robel Kahsay, Jonathon Keeney and 5 more

Open access · hybridAbstract read
In one paragraph

Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
2.6field-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

28 citing papers in PubMed, 46 citations in OpenAlex.

  1. Article
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  4. A competing TMPO-AS1-let-7b-5p-kinesin superfamily RNA network predicts poor lung cancer patient survival.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026
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  9. TMPO-AS1-hsa-let-7b-5p-EZH2-RNA network predicts poor survival in basal-like breast cancer patients.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025
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  17. Building a large gene expression-cancer knowledge base with limited human annotations.Database : the journal of biological databases and curation · 2023
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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

15 authors at 4 institutions in 2 countries.

Hayley M DingerdissenThe George Washington University, Washington DC.
Frederic BastianSIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
K Vijay-ShankerUniversity of Delaware, Newark, DE.
Marc Robinson-RechaviSIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Amanda BellThe George Washington University, Washington DC.
Nikhita GogateThe George Washington University, Washington DC.
Samir GuptaUniversity of Delaware, Newark, DE.
Evan HolmesThe George Washington University, Washington DC.
Robel KahsayThe George Washington University, Washington DC.
Jonathon KeeneyThe George Washington University, Washington DC.
Heather KincaidNASA Jet Propulsion Laboratory, Pasadena, CA.
Charles Hadley KingThe George Washington University, Washington DC.
David LiuNASA Jet Propulsion Laboratory, Pasadena, CA.
Daniel J CrichtonNASA Jet Propulsion Laboratory, Pasadena, CA.
Raja MazumderThe George Washington University, Washington DC.
George Washington University · USJet Propulsion Laboratory · USSIB Swiss Institute of Bioinformatics · CHUniversity of Delaware · US

Funding

Integration of comprehensive cancer mutation and expression-associated data for biomarker evaluation and discoveryU01CA215010 · NCI · GEORGE WASHINGTON UNIVERSITY · PI CRICHTON, DANIEL, MAZUMDER, RAJA · 2017 to 2019
$1.3M
NCI NIH HHS U01 CA215010
6 · The paper itself

Abstract

purposeThe purpose of OncoMX

methodsCancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database.

resultsOncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs.

conclusionOncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

Indexed as

Knowledge BasesSoftwareAnimalsBiological OntologiesBiomarkers, TumorComputational BiologyDatabases, GeneticData MiningHumansMiceNeoplasmsUser-Computer InterfaceBiomarkers, Tumor

Identifiers

PMID32142370
PMCPMC7101249
OpenAlexW3010327427

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.