ArticleJournal of integrative bioinformatics2020
Metalearning approach for leukemia informative genes prioritization.
Article in Journal of integrative bioinformatics, 2020. 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.
- AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia.Biomedical engineering and computational biology · 2026Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
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2 authors.
Funding
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Abstract
The discovery of diagnostic or prognostic biomarkers is fundamental to optimize therapeutics for patients. By enhancing the interpretability of the prediction model, this work is aimed to optimize Leukemia diagnosis while retaining a high-performance evaluation in the identification of informative genes. For this purpose, we used an optimal parameterization of Kernel Logistic Regression method on Leukemia microarray gene expression data classification, applying metalearners to select attributes, reducing the data dimensionality before passing it to the classifier. Pearson correlation and chi-squared statistic were the attribute evaluators applied on metalearners, having information gain as single-attribute evaluator. The implemented models relied on 10-fold cross-validation. The metalearners approach identified 12 common genes, with highest average merit of 0.999. The practical work was developed using the public datamining software WEKA.
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