Evidence map›Paper›PMID 35891786›Full record

ArticleComputational and structural biotechnology journal2022

MSPJ: Discovering potential biomarkers in small gene expression datasets

HuaChun Yin, JingXin Tao, Yuyang Peng, Ying Xiong, Bo Li, Song Li, Hui Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2022. 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. 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

7 authors.

HuaChun YinDepartment of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China.
JingXin TaoCollege of Life Sciences, Chongqing Normal University, Chongqing 401331, China.
Yuyang PengDepartment of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China.
Ying XiongDepartment of Neurobiology, Chongqing Key Laboratory of Neurobiology, The Army Medical University, Chongqing 400038, China.
Bo LiCollege of Life Sciences, Chongqing Normal University, Chongqing 401331, China.
Song LiDepartment of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China.
Hui YangDepartment of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In transcriptomics, differentially expressed genes (DEGs) provide fine-grained phenotypic resolution for comparisons between groups and insights into molecular mechanisms underlying the pathogenesis of complex diseases or phenotypes. The robust detection of DEGs from large datasets is well-established. However, owing to various limitations (e.g., the low availability of samples for some diseases or limited research funding), small sample size is frequently used in experiments. Therefore, methods to screen reliable and stable features are urgently needed for analyses with limited sample size. In this study, MSPJ, a new machine learning approach for identifying DEGs was proposed to mitigate the reduced power and improve the stability of DEG identification in small gene expression datasets. This ensemble learning-based method consists of three algorithms: an improved multiple random sampling with

Indexed as

AUC, area under the ROC curve (AUC)DEGs, differentially expressed genesDifferentially expressed genesFDR, false positive rateFeature selectionGA, genetic algorithmGEO, Gene Expression OmnibusGO, gene ontologyMachine learningmRMR, minimum-redundancy-maximum-relevanceMSPJ, the Joint method of Meta-analysis, SVM-RFE, and Permutation testRandom samplingRF, random forestROC, receiver operating characteristicSAM, significance analysis of microarraysSmall sample sizeSMDs, standardized mean differencesSNR, signal noise ratioSVM-RFE, support vector machines-recursive feature elimination

Identifiers

PMID35891786
PMCPMC9304602

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.