ArticleBiotech (Basel (Switzerland))2023
Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data.
Article in Biotech (Basel (Switzerland)), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 citing papers in PubMed.
- Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data.Journal of medical signals and sensors · 2026Article
- Statistical Models for High-Risk Intestinal Metaplasia with DNA Methylation Profiling.Epigenomes · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios, including variable screening, model selection, order selection for response categories, and variable selection. We perform our procedure on high-dimensional gene expression data with 801 patients, 2426 genes, and five types of cancerous tumors. As a result, we recommend three finalized models: one with 74 genes achieves extremely low cross-entropy loss and zero predictive error rate based on a five-fold cross-validation; and two other models with 31 and 4 genes, respectively, are recommended for prognostic multi-gene signatures.
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Registered trials
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