ArticleSIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining2012
Sparse Methods for Biomedical Data.
Article in SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Addictions are linked but distinct: A transdiagnostic network analysis of concurrent disorder risk.Journal of behavioral addictions · 2026Article
- Multi-omics data integration using time-to event endpoint and supervised Cox penalized regression: a comprehensive review.Clinical and experimental medicine · 2026Review
- Unifying equivalences across unsupervised learning, network science, and imaging/network neuroscience.ArXiv · 2025Article
- Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks.Nucleic acids research · 2023Article
- Evolutionary Sparse Learning for Phylogenomics.Molecular biology and evolution · 2021Article
- Application of graphical lasso in estimating network structure in gene set.Annals of translational medicine · 2020Article
- Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data.PLoS computational biology · 2019Article
- Robust clinical marker identification for diabetic kidney disease with ensemble feature selection.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2018Article
- Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia.JAMIA open · 2018Article
- Functional brain networks reconstruction using group sparsity-regularized learning.Brain imaging and behavior · 2018Article
- Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson's Disease.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2016Article
- Article
- Joint impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization.PloS one · 2015Article
- Evolutionary Diagnosis of non-synonymous variants involved in differential drug response.BMC medical genomics · 2015Article
- Review
- Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression.Bioinformatics (Oxford, England) · 2014Article
- A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems.JMLR workshop and conference proceedings · 2013Article
Corrections and comments
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2 authors.
Funding
Abstract
Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical data are often sparse. For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway involves only a small number of genes; many biomedical signals are sparse or compressible in the sense that they have concise representations when expressed in a proper basis. Therefore, finding sparse representations is fundamentally important for scientific discovery. Sparse methods based on the [Formula: see text] norm have attracted a great amount of research efforts in the past decade due to its sparsity-inducing property, convenient convexity, and strong theoretical guarantees. They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging. In this paper, we review state-of-the-art sparse methods and their applications to biomedical data.
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Registered trials
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.