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https://doi.org/10.1214/10-aoas377
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.csda.2006.12.019openalex-snapshot
Relaxed Lasso
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1038/sj.bjc.6602856openalex-snapshot
S100A4 (Mts1) gene overexpression is associated with invasion and metastasis of papillary thyroid carcinoma
inferredcites →https://doi.org/10.1073/pnas.0608396103openalex-snapshot
Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target
inferredcites →https://doi.org/10.1093/biostatistics/kxm010openalex-snapshot
Penalized logistic regression for detecting gene interactions
inferredcites →https://doi.org/10.1093/carcin/bgm147openalex-snapshot
Involvement of visinin-like protein-1 (VSNL-1) in regulating proliferative and invasive properties of neuroblastoma
inferredcites →https://doi.org/10.1093/jnen/63.3.210openalex-snapshot
Regulators of G-Protein Signaling 3 and 4 (RGS3, RGS4) Are Associated with Glioma Cell Motility
inferredcites →https://doi.org/10.1111/j.1467-9868.2005.00503.xopenalex-snapshot
Regularization and Variable Selection Via the Elastic Net
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1158/0008-5472.can-04-0452openalex-snapshot
Gene Expression Profiling of Gliomas Strongly Predicts Survival
inferredcites →https://doi.org/10.1198/016214501753382273openalex-snapshot
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
inferredcites →https://doi.org/10.1198/016214506000000735openalex-snapshot
The Adaptive Lasso and Its Oracle Properties
inferredcites →https://doi.org/10.1198/016214508000000481openalex-snapshot
Variable Inclusion and Shrinkage Algorithms
inferredcites →https://doi.org/10.2307/1269730openalex-snapshot
Better Subset Regression Using the Nonnegative Garrote

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Novel Scalar-on-matrix Regression for Unbalanced Feature Matrices
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Short- and Long-Run Determinants of the Price Behavior of Us Clean Energy Stocks: A Dynamic Ardl Simulations Approach
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Gene selection and classification of microarray gene expression data based on a new adaptive L1-norm elastic net penalty
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RHDSI: A novel dimensionality reduction based algorithm on high dimensional feature selection with interactions
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Dynamic tilted current correlation for high dimensional variable screening
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A ranking-based strategy to prune variable selection ensembles
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Early stopping aggregation in selective variable selection ensembles for high-dimensional linear regression models
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Which opinion is more trustworthy: An analysts’ earnings forecast quality assessment framework based on machine learning
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Randomizing outputs to increase variable selection accuracy
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Coordinated regulation by lncRNAs results in tight lncRNA-target couplings
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Protocol for evaluating mechanistic pathways associated with HIV acquisition via nested Least Absolute Shrinkage and Selective Operator analysis
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Job Opportunities and Academic Dropout: The Case of the University of Trieste
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How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion.
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Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients
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Brain structural connectivity and neuroticism in healthy adults
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Development and validation of an RNA-seq-based transcriptomic risk score for asthma
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Scalable and robust machine learning framework for HIV classification using clinical and laboratory data
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A novel bagging approach for variable ranking and selection via a mixed importance measure
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Ensemble of penalized logistic models for classification of high-dimensional data
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Robust variable selection based on the random quantile LASSO
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Bayesian reciprocal LASSO quantile regression
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A feature selection method for classification based on ensemble of penalized logistic models
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Penalized regression analysis with individual-specific patterns of missing covariates
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Stochastic Stepwise Ensembles for Variable Selection
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Tilted Correlation Screening Learning in High-Dimensional Data Analysis
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Influence Diagnostics for High-Dimensional Lasso Regression
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A Stability Framework for Parameter Selection in the Minimum Covariance Determinant Problem
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Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level
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Robust Sample-Specific Stability Selection with Effective Error Control
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PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data
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selectBoost: a general algorithm to enhance the performance of variable selection methods
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A generalized framework for controlling FDR in gene regulatory network inference
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A network analysis of affective and motivational individual differences and error monitoring in a non-clinical sample
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Exosomes in mammals with greater habitat variability contain more proteins and RNAs
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Stability selection enables robust learning of differential equations from limited noisy data
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inferred← citeshttps://doi.org/10.1111/rssb.12316openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2022/2249447openalex-snapshot
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inferred← citeshttps://doi.org/10.1158/0008-5472.can-21-3113openalex-snapshot
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inferred← citeshttps://doi.org/10.1172/jci.insight.170767openalex-snapshot
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PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data
inferred← citeshttps://doi.org/10.1186/s12859-019-2915-1openalex-snapshot
Difficulty in inferring microbial community structure based on co-occurrence network approaches
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Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)
inferred← citeshttps://doi.org/10.1186/s12864-019-6298-5openalex-snapshot
D3GRN: a data driven dynamic network construction method to infer gene regulatory networks
inferred← citeshttps://doi.org/10.1186/s12874-016-0197-0openalex-snapshot
The case-crossover design via penalized regression
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Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
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inferred← citeshttps://doi.org/10.1214/21-aoas1487openalex-snapshot
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inferred← citeshttps://doi.org/10.1214/21-ba1275openalex-snapshot
An Ensemble EM Algorithm for Bayesian Variable Selection
inferred← citeshttps://doi.org/10.1214/21-ejs1952openalex-snapshot
Aggregated hold out for sparse linear regression with a robust loss function
inferred← citeshttps://doi.org/10.1214/25-aos2591openalex-snapshot
Finite- and large sample inference for model and coefficients in high-dimensional linear regression with repro samples
inferred← citeshttps://doi.org/10.13189/ujph.2021.090514openalex-snapshot
Improving the Diagnosis of Breast Cancer Using Regularized Logistic Regression with Adaptive Elastic Net
inferred← citeshttps://doi.org/10.1371/journal.pone.0134151openalex-snapshot
Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents
inferred← citeshttps://doi.org/10.1371/journal.pone.0141869openalex-snapshot
Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets
inferred← citeshttps://doi.org/10.1371/journal.pone.0246159openalex-snapshot
HDSI: High dimensional selection with interactions algorithm on feature selection and testing
inferred← citeshttps://doi.org/10.1371/journal.pone.0278570openalex-snapshot
Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data
inferred← citeshttps://doi.org/10.1371/journal.pone.0344198openalex-snapshot
Network-constrained Random Lasso for biologically interpretable gene network inference across unequal sample sizes
inferred← citeshttps://doi.org/10.1504/ijdmb.2018.094891openalex-snapshot
DiffGRN: differential gene regulatory network analysis
inferred← citeshttps://doi.org/10.1515/ijb-2022-0025openalex-snapshot
Penalized logistic regression with prior information for microarray gene expression classification
inferred← citeshttps://doi.org/10.2196/75862openalex-snapshot
Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study
inferred← citeshttps://doi.org/10.29220/csam.2022.29.6.629openalex-snapshot
Ensemble variable selection using genetic algorithm
inferred← citeshttps://doi.org/10.3389/fgene.2024.1481787openalex-snapshot
CSER: a gene regulatory network construction method based on causal strength and ensemble regression
inferred← citeshttps://doi.org/10.3389/fnins.2016.00108openalex-snapshot
Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity
inferred← citeshttps://doi.org/10.3390/ani13071193openalex-snapshot
Importance of Mid-Infrared Spectra Regions for the Prediction of Mastitis and Ketosis in Dairy Cows
inferred← citeshttps://doi.org/10.3390/computers10020018openalex-snapshot
Network Analysis of Local Gene Regulators in Arabidopsis thaliana under Spaceflight Stress
inferred← citeshttps://doi.org/10.3390/computers13040093openalex-snapshot
GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification
inferred← citeshttps://doi.org/10.3390/jpm12081330openalex-snapshot
Selecting Genetic Variants and Interactions Associated with Amyotrophic Lateral Sclerosis: A Group LASSO Approach
inferred← citeshttps://doi.org/10.3390/math11173738openalex-snapshot
LASSO and Elastic Net Tend to Over-Select Features
inferred← citeshttps://doi.org/10.3390/metabo13020161openalex-snapshot
Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review
inferred← citeshttps://doi.org/10.4018/978-1-5225-8903-7.ch038openalex-snapshot
Identification of Candidate Genes Responsible for Age-Related Macular Degeneration Using Microarray Data
inferred← citeshttps://doi.org/10.4018/ijssmet.2018040102openalex-snapshot
Identification of Candidate Genes Responsible for Age-related Macular Degeneration using Microarray Data
inferred← citeshttps://doi.org/10.48550/arxiv.1512.03307openalex-snapshot
AcSel: selecting variables with accuracy in correlated datasets
inferred← citeshttps://doi.org/10.51202/0342-3743-2021-1-2-045openalex-snapshot
ZWISCHEN TRADITION UND MODERNE
inferred← citeshttps://doi.org/10.5194/aab-61-451-2018openalex-snapshot
The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO
inferred← citeshttps://doi.org/10.5539/mas.v9n4p170openalex-snapshot
Adjusted Adaptive LASSO in High-dimensional Poisson Regression Model
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684217openalex-snapshot
Additional file 1 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684220openalex-snapshot
Additional file 2 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684223openalex-snapshot
Additional file 3 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684226openalex-snapshot
Additional file 4 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684229openalex-snapshot
Additional file 5 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684232openalex-snapshot
Additional file 6 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684235openalex-snapshot
Additional file 7 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684238openalex-snapshot
Additional file 8 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684241openalex-snapshot
Additional file 9 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684244openalex-snapshot
Additional file 10 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684247openalex-snapshot
Additional file 11 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684250openalex-snapshot
Additional file 12 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684253openalex-snapshot
Additional file 13 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684256openalex-snapshot
Additional file 14 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.6084/m9.figshare.26684259openalex-snapshot
Additional file 15 of Biochar improves the nutrient cycle in sandy-textured soils and increases crop yield: a systematic review
inferred← citeshttps://doi.org/10.61186/jss.17.2.10openalex-snapshot
Identification of Influential Observations for High-Dimensional Regression
inferred← citeshttps://doi.org/10.70675/219dc6f8z5151z49e1zab5bz19f8c2ffeb4fopenalex-snapshot
Personalized drug adverse side effect prediction
inferred← citeshttps://doi.org/10.70675/708e64e4zde41z44b0zb0c3z69564ef76037openalex-snapshot
Sélection de variables et régression sur les quantiles
inferred← citeshttps://doi.org/10.70675/7e6c3afdze3fcz40afzac62zdff3dafcc022openalex-snapshot
Hold-out et agrégation d'hold-out
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.