ArticlePloS one2016
Feature Selection and Cancer Classification via Sparse Logistic Regression with the Hybrid L1/2 +2 Regularization.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression.BMC bioinformatics · 2022Pooled it
- Biomarker identification of triple negative breast cancer subtypes using machine learning.NPJ systems biology and applications · 2026Article
- Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data.Journal of medical signals and sensors · 2026Article
- Deep-GenMut: Automated genetic mutation classification in oncology: A deep learning comparative study.Heliyon · 2024Article
- Chronic Obstructive Pulmonary Disease: Novel Genes Detection with Penalized Logistic Regression.Cell journal · 2023Article
- Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization.BMC bioinformatics · 2023Article
- Prediction models with graph kernel regularization for network data.Journal of applied statistics · 2023Article
- Identifying common transcriptome signatures of cancer by interpreting deep learning models.Genome biology · 2022Article
- Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Integrating molecular interactions and gene expression to identify biomarkers and network modules of chronic obstructive pulmonary disease.Technology and health care : official journal of the European Society for Engineering and 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
- Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.Journal of healthcare engineering · 2021Article
- Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model.Technology and health care : official journal of the European Society for Engineering and Medicine · 2021Article
- LogSum + LScientific reports · 2020Article
- CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data.PLoS computational biology · 2020Article
- Sparse Logistic Regression WithFrontiers in neuroinformatics · 2020Article
- Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors.Frontiers in genetics · 2019Article
- Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection.PloS one · 2019Article
- Collaborative representation-based classification of microarray gene expression data.PloS one · 2017Article
- Sparse Bayesian classification and feature selection for biological expression data with high correlations.PloS one · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data. Although logistic regression is one of the most popular classification methods, it does not induce feature selection. In this paper, we presented a new hybrid L1/2 +2 regularization (HLR) function, a linear combination of L1/2 and L2 penalties, to select the relevant gene in the logistic regression. The HLR approach inherits some fascinating characteristics from L1/2 (sparsity) and L2 (grouping effect where highly correlated variables are in or out a model together) penalties. We also proposed a novel univariate HLR thresholding approach to update the estimated coefficients and developed the coordinate descent algorithm for the HLR penalized logistic regression model. The empirical results and simulations indicate that the proposed method is highly competitive amongst several state-of-the-art methods.
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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.