ArticleScientific reports2020
Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review.Journal of cancer survivorship : research and practice · 2025Pooled it
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- Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features.Scientific reports · 2025Article
- Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection.Scientific reports · 2024Article
- Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study.Scientific reports · 2024Article
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Using random forest algorithm for glomerular and tubular injury diagnosis.Frontiers in medicine · 2022Article
- Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province.Frontiers in medicine · 2022Article
- Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods.Scientific reports · 2021Article
- Dimensionality reduction using singular vectors.Scientific reports · 2021Article
- Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.Computational and mathematical methods in medicine · 2021Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
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4 authors.
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Abstract
Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to identify gene signatures that are related to a certain disease from high-dimensional gene expression data. The expression of genes may have grouping structures, for example, a group of co-regulated genes that have similar biological functions tend to have similar expressions. Thus it is preferable to take the grouping structure into consideration to select features. In this paper, we propose a Bayesian Robit regression method with Hyper-LASSO priors (shortened by BayesHL) for feature selection in high dimensional genomic data with grouping structure. The main features of BayesHL include that it discards more aggressively unrelated features than LASSO, and it makes feature selection within groups automatically without a pre-specified grouping structure. We apply BayesHL in gene expression analysis to identify subsets of genes that contribute to the 5-year survival outcome of endometrial cancer (EC) patients. Results show that BayesHL outperforms alternative methods (including LASSO, group LASSO, supervised group LASSO, penalized logistic regression, random forest, neural network, XGBoost and knockoff) in terms of predictive power, sparsity and the ability to uncover grouping structure, and provides insight into the mechanisms of multiple genetic pathways leading to differentiated EC survival outcome.
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