Evidence map›Paper›PMID 27896992›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2017

MICRORNA-AUGMENTED PATHWAYS (mirAP) AND THEIR APPLICATIONS TO PATHWAY ANALYSIS AND DISEASE SUBTYPING.

Diana Diaz, Michele Donato, Tin Nguyen, Sorin Draghici

Abstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Diana DiazDepartment of Computer Science, Wayne State University, Detroit, MI 48202, U.S.A.
Michele Donato
Tin Nguyen
Sorin Draghici

Funding

Pathway-Guide: A novel tool for the analysis of signaling and metabolic pathwaysR42GM087013 · NIGMS · ADVAITA CORPORATION · PI DRAGHICI, SORIN · 2011 to 2013
$2.2M
Novel methods for the analysis of gene signaling pathways with applications in obR01DK089167 · NIDDK · WAYNE STATE UNIVERSITY · PI DRAGHICI, SORIN · 2010 to 2013
$1.2M
A Novel Method for Signaling Pathway AnalysisR41GM087013 · NIGMS · ADVAITA CORPORATION · PI DRAGHICI, SORIN · 2009 to 2009
$146k
NIDDK NIH HHS R01 DK089167NIGMS NIH HHS R41 GM087013NIGMS NIH HHS R42 GM087013
6 · The paper itself

Abstract

MicroRNAs play important roles in the development of many complex diseases. Because of their importance, the analysis of signaling pathways including miRNA interactions holds the potential for unveiling the mechanisms underlying such diseases. However, current signaling pathway databases are limited to interactions between genes and ignore miRNAs. Here, we use the information on miRNA targets to build a database of miRNA-augmented pathways (mirAP), and we show its application in the contexts of integrative pathway analysis and disease subtyping. Our miRNA-mRNA integrative pathway analysis pipeline incorporates a topology-aware approach that we previously implemented. Our integrative disease subtyping pipeline takes into account survival data, gene and miRNA expression, and knowledge of the interactions among genes. We demonstrate the advantages of our approach by analyzing nine sample-matched datasets that provide both miRNA and mRNA expression. We show that integrating miRNAs into pathway analysis results in greater statistical power, and provides a more comprehensive view of the underlying phenomena. We also compare our disease subtyping method with the state-of-the-art integrative analysis by analyzing a colorectal cancer database from TCGA. The colorectal cancer subtypes identified by our approach are significantly different in terms of their survival expectation. These miRNA-augmented pathways offer a more comprehensive view and a deeper understanding of biological pathways. A better understanding of the molecular processes associated with patients' survival can help to a better prognosis and an appropriate treatment for each subtype.

Indexed as

Gene Regulatory NetworksAlgorithmsComputational BiologyDatabases, Nucleic AcidDiseaseGene Expression ProfilingHumansMicroRNAsRNA, MessengerSignal TransductionMicroRNAsRNA, Messenger

Identifiers

PMID27896992
PMCPMC5147738

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Registered trials

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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.