Evidence map›Paper›PMID 35439942›Full record

ArticleBMC bioinformatics2022

CoMI: consensus mutual information for tissue-specific gene signatures.

Sing-Han Huang, Yu-Shu Lo, Yong-Chun Luo, Yi-Hsuan Chuang, Jung-Yu Lee, Jinn-Moon Yang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 85% 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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Review
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 at 1 institution in 1 country.

Sing-Han HuangInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Yu-Shu LoInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Yong-Chun LuoInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Yi-Hsuan ChuangInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Jung-Yu LeeInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Jinn-Moon YangInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan. moon@faculty.nctu.edu.tw.ORCID http://orcid.org/0000-0002-3205-4391
National Yang Ming Chiao Tung University · TW

Funding

Ministry of Science and Technology, Taiwan MOST 109-2321-B-009-007Ministry of Science and Technology, Taiwan MOST 109-2634-F-009-021National Health Research Institutes NHRI-EX109-10504PI
6 · The paper itself

Abstract

backgroundThe gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures.

resultsHere, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis.

conclusionsOur results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.

Indexed as

Gene Expression Regulation, NeoplasticNeoplasmsConsensusGene Expression ProfilingHumansPrecision MedicineOmics dataPrognostic gene signatureTissue-specific gene signature

Identifiers

PMID35439942
PMCPMC9019939
OpenAlexW4224325451

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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.