ArticleBioMed research international2017
Robust Significance Analysis of Microarrays by Minimum
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Robust hierarchical co-clustering for exploring toxicogenomic biomarkers and their chemical regulators.Scientific reports · 2025Article
- Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.Medicina (Kaunas, Lithuania) · 2023Article
- Integrated Gene Expression and Methylation Analyses Identify DLL3 as a Biomarker for Prognosis of Malignant Glioma.Journal of molecular neuroscience : MN · 2021Article
- Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer.Frontiers in genetics · 2020Article
- A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs.Medicina (Kaunas, Lithuania) · 2019Article
- Screening of schizophrenia associated miRNAs and the regulation of miR-320a-3p on integrin β1.Medicine · 2019Article
- Robust Feature Selection Approach for Patient Classification using Gene Expression Data.Bioinformation · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum
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Registered trials
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