Evidence map›Paper›PMID 28582486›Full record

ArticleBioinformatics (Oxford, England)2017

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

Jiadong Ji, Di He, Yang Feng, Yong He, Fuzhong Xue, Lei Xie

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

6 authors.

Jiadong JiDepartment of Mathematical Statistics, School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China.
Di HePh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.
Yang FengDepartment of Statistics, Columbia University, New York, NY 10027, USA.
Yong HeDepartment of Mathematical Statistics, School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China.
Fuzhong XueDepartment of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China.
Lei XiePh.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY 10016, USA.

Funding

Basic and Translational Research Center for Reducing Health DisparitiesG12MD007599 · NIMHD · HUNTER COLLEGE · PI ANGULO, JESUS A · 2012 to 2017
$14.6M
Drug discovery by integrating chemical genomics and structural systems biologyR01LM011986 · NLM · HUNTER COLLEGE · PI BRINKMAN, FIONA, BURLEY, STEPHEN K · 2014 to 2017
$1.2M
NIMHD NIH HHS G12 MD007599NLM NIH HHS R01 LM011986
6 · The paper itself

Abstract

motivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.

resultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. AVAILABILITY AND IMPLEMENTATION: R scripts available at https://github.com/jijiadong/JDINAC. CONTACT: lxie@iscb.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Gene Regulatory NetworksBreast NeoplasmsCarcinomaComputer SimulationFemaleHumans

Identifiers

PMID28582486
PMCPMC5870609

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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.