ArticleBioinformatics (Oxford, England)2019
Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 72 citations in OpenAlex.
- Unsupervised Feature Selection via Self-Supervised HSIC and Elastic Net Regularization.Entropy (Basel, Switzerland) · 2026Article
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- Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods.BMC pregnancy and childbirth · 2025Article
- Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.Frontiers in big data · 2025Review
- Identification of novel hypertension biomarkers using explainable AI and metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship.PLOS digital health · 2024Article
- Analysis and validation of potential ICD-related biomarkers in development of myopia using machine learning.International ophthalmology · 2024Article
- RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery.Neuropathology and applied neurobiology · 2023Article
- "Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation.Journal of the American College of Radiology : JACR · 2023Review
- A moment kernel machine for clinical data mining to inform medical decision making.Scientific reports · 2023Article
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- Predicting yield of individual field-grown rapeseed plants from rosette-stage leaf gene expression.PLoS computational biology · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
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- Variation of DNA methylation on theRegenerative therapy · 2022Article
- Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.PLoS computational biology · 2022Article
- EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm.Entropy (Basel, Switzerland) · 2022Article
- Feature selection for kernel methods in systems biology.NAR genomics and bioinformatics · 2022Article
- Where Do We Stand in Regularization for Life Science Studies?Journal of computational biology : a journal of computational molecular cell biology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationFinding non-linear relationships between biomolecules and a biological outcome is computationally expensive and statistically challenging. Existing methods have important drawbacks, including among others lack of parsimony, non-convexity and computational overhead. Here we propose block HSIC Lasso, a non-linear feature selector that does not present the previous drawbacks.
resultsWe compare block HSIC Lasso to other state-of-the-art feature selection techniques in both synthetic and real data, including experiments over three common types of genomic data: gene-expression microarrays, single-cell RNA sequencing and genome-wide association studies. In all cases, we observe that features selected by block HSIC Lasso retain more information about the underlying biology than those selected by other techniques. As a proof of concept, we applied block HSIC Lasso to a single-cell RNA sequencing experiment on mouse hippocampus. We discovered that many genes linked in the past to brain development and function are involved in the biological differences between the types of neurons. AVAILABILITY AND IMPLEMENTATION: Block HSIC Lasso is implemented in the Python 2/3 package pyHSICLasso, available on PyPI. Source code is available on GitHub (https://github.com/riken-aip/pyHSICLasso). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.