Evidence map›Paper›PMID 26201006›Full record

ArticlePloS one2015

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.

Jian Liu, Jin-Xing Liu, Ying-Lian Gao, Xiang-Zhen Kong, Xue-Song Wang, Dong Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Joint Lp-Norm and LFrontiers in genetics · 2021
    Article
  2. Article
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.

Jian LiuSchool of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China; School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221000, Jiangsu, China.
Jin-Xing LiuSchool of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China; Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China.
Ying-Lian GaoLibrary of Qufu Normal University, Qufu Normal University, Rizhao, 276826, Shandong, China.
Xiang-Zhen KongSchool of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China.
Xue-Song WangSchool of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221000, Jiangsu, China; The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation Chinese Academy of Sciences, Beijing, 100000, China.
Dong WangSchool of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.

Indexed as

Genes, PlantAlgorithmsComputer SimulationGene Expression ProfilingGene Expression Regulation, PlantOligonucleotide Array Sequence AnalysisPlants

Identifiers

PMID26201006
PMCPMC4511795

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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.