ArticleBioinformation2011
Microarray data analysis and mining tools.
Article in Bioinformation, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Enhancing Transcriptomic Insights into Neurological Disorders Through the Comparative Analysis of Shapley Values.Current issues in molecular biology · 2024Article
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- A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization.Sensors (Basel, Switzerland) · 2022Article
- Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model.Journal of immunology (Baltimore, Md. : 1950) · 2016Article
- Data mining strategies to improve multiplex microbead immunoassay tolerance in a mouse model of infectious diseases.PloS one · 2015Article
- Identification of clinical phenotypes using cluster analyses in COPD patients with multiple comorbidities.BioMed research international · 2014Review
- Insights from the clustering of microarray data associated with the heart disease.Bioinformation · 2013Article
- Building gene expression profile classifiers with a simple and efficient rejection option in R.BMC bioinformatics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously. Arrays have been applied to studies in gene expression, genome mapping, SNP discrimination, transcription factor activity, toxicity, pathogen identification and many other applications. In this paper we concentrate on discussing various bioinformatics tools used for microarray data mining tasks with its underlying algorithms, web resources and relevant reference. We emphasize this paper mainly for digital biologists to get an aware about the plethora of tools and programs available for microarray data analysis. First, we report the common data mining applications such as selecting differentially expressed genes, clustering, and classification. Next, we focused on gene expression based knowledge discovery studies such as transcription factor binding site analysis, pathway analysis, protein- protein interaction network analysis and gene enrichment analysis.
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Registered trials
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.