ArticleProceedings of machine learning research2021
LassoNet: Neural Networks with Feature Sparsity.
Article in Proceedings of machine learning research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 48 citations in OpenAlex.
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- Hierarchical sparse spatiotemporal graph neural network for brain graph classification.iScience · 2026Article
- Uncertainty-aware instance-wise feature selection with adaptive graph regularization.Scientific reports · 2026Article
- Integrative multi-omics analysis reveals host-microbiome metabolic alterations and candidate biomarkers in Parkinson's disease.BMC microbiology · 2026Article
- A fault diagnosis method for complex systems based on hierarchical belief rule base with One-vs-Rest strategy.Scientific reports · 2026Article
- Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review.Toxics · 2026Review
- Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data.Briefings in bioinformatics · 2026Article
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
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- Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients.Frontiers in oncology · 2026Article
- Sparse-Input Neural Network using Group Concave Regularization.Transactions on machine learning research · 2025Article
- Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review.Journal of clinical medicine · 2025Review
- Identification of novel biomarkers for epithelial ovarian cancer through machine learning and explainable artificial intelligence using in silico and in vitro analysis.Scientific reports · 2025Article
- UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.Nature communications · 2025Article
- Explainability and importance estimate of time series classifier via embedded neural network.Scientific reports · 2025Article
- Decoding ancestry-specific genetic risk: interpretable deep feature selection reveals prostate cancer SNP disparities in diverse populations.BioData mining · 2025Article
- GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.Bioinformatics (Oxford, England) · 2025Article
- A Framework for Predicting Winter Wheat Yield in Northern China with Triple Cross-Attention and Multi-Source Data Fusion.Plants (Basel, Switzerland) · 2025Article
- Multi-task genomic prediction using gated residual variable selection neural networks.BMC bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Much work has been done recently to make neural networks more interpretable, and one approach is to arrange for the network to use only a subset of the available features. In linear models, Lasso (or
Identifiers
36092461PMC9453696W3172710683What OpenQuestion holds
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.