Evidence map›Paper›PMID 25870785›Full record

ArticleFEBS open bio2015

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

Jin Li, Limei Wang, Maozu Guo, Ruijie Zhang, Qiguo Dai, Xiaoyan Liu, Chunyu Wang, Zhixia Teng, Ping Xuan, Mingming Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in FEBS open bio, 2015. 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

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

4 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

10 authors.

Jin LiSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China ; School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China ; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Limei WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China ; School of Basic Medical Sciences, Harbin Medical University, Harbin, Heilongjiang, China.
Maozu GuoSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Ruijie ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Qiguo DaiSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Xiaoyan LiuSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Chunyu WangSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Zhixia TengSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Ping XuanSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Mingming ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Indexed as

AD, Alzheimer diseaseCo-regulation networkDisease gene miningeQTLeQTLs, expression quantitative trait lociGGCRN, gene–gene co-regulation networkHPRD, Human Protein Reference DatabaseKEGG, Kyoto Encyclopedia of Genes and GenomesPPI, protein–protein interactionProtein–protein interactionRandom walk with restartRWR, random walk with restartSNP, single-nucleotide polymorphism

Identifiers

PMID25870785
PMCPMC4392065

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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.