ArticleGenes2022
A Python Clustering Analysis Protocol of Genes Expression Data Sets.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Transcriptomic Analysis Reveals the Antioxidant and Anti-Inflammatory Mechanisms of EGCG-Zn Nanoparticles in Dextran Sulfate Sodium-Induced Colitis in Mice.Antioxidants (Basel, Switzerland) · 2026Article
- Prophage landscapes in clinical MRSA: safety profiling and discovery of Lys81, a broad-spectrum bacteriolytic enzyme.Frontiers in microbiology · 2026Article
- Pharmacogenomics: Challenges and Future.Genes · 2024Article
- Comparative Transcriptome and Widely Targeted Metabolome Analysis Reveals the Molecular Mechanism of Powdery Mildew Resistance in Tomato.International journal of molecular sciences · 2023Article
- Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.Cancer medicine · 2023Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.
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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.