Evidence map›Paper›PMID 31878868›Full record

ArticleBMC bioinformatics2019

PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets.

Dvir Netanely, Neta Stern, Itay Laufer, Ron Shamir

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. BioMark: biomarker analysis tool.BMC bioinformatics · 2026
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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

4 authors.

Dvir NetanelyBlavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Neta SternBlavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Itay LauferBlavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Ron ShamirBlavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. rshamir@tau.ac.il.ORCID http://orcid.org/0000-0003-1889-9870

Funding

Israel Cancer Association Donation of Avraham RotsteinIsrael Science Foundation (ISF) 1339/18Israel Science Foundation - National Natural Science Foundation of China joint program 2193/15United State - Israel Binational Science Foundation (BSF) and the United States National Science Foundation (NSF) 2016694
6 · The paper itself

Abstract

backgroundAnalysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis.

resultsWe developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA.

conclusionsPROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

Indexed as

SoftwareGenomicsHumansNeoplasmsCancer biomarkersCancer genomicsCancer subtypesGene expression analysisMulti-omic clusteringMulti-omicsPersonalized medicine

Identifiers

PMID31878868
PMCPMC6933892

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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.