A Sparse-Group Lasso

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works

Regression shrinkage and selection via the lasso

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Regularization and Variable Selection Via the Elastic Net

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Regularization Paths for Generalized Linear Models via Coordinate Descent

Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works

Model Selection and Estimation in Regression with Grouped Variables

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The Group Lasso for Logistic Regression

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A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen

Ma, X.-J., Wang, Z., Ryan, P. D., Isakoff, S. J., Barmettler, A., Fuller, A. P., Muir, B., Mohapatra, G., Salunga, R. C., Tuggle, J. T., et al. (2004) · Cancer Cell · doi:10.1016/j.ccr.2004.05.015 · Cited by 945 · see citing works

NESTA: A Fast and Accurate First-Order Method for Sparse Recovery

Becker, S., Bobin, J., & Candès, E. J. (2009) · Cited by 897 · see citing works

Gradient methods for minimizing composite objective function

Nesterov, Y. E. (2007) · Cited by 867 · see citing works

Group lasso with overlap and graph lasso

Jacob, L., Obozinski, G., & Vert, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553431 · Cited by 842 · see citing works

A note on the group lasso and a sparse group lassoPreprint

Friedman, J. H., Hastie, T., & Tibshirani, R. J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.0736 · Cited by 645 · see citing works

Molecular Classification of Crohn's Disease and Ulcerative Colitis Patients Using Transcriptional Profiles in Peripheral Blood Mononuclear Cells

Burczynski, M. E., Peterson, R. L., Twine, N. C., Zuberek, K., Brodeur, B. J., Casciotti, L., Maganti, V., Reddy, P., Strahs, A. L., Immermann, F., et al. (2006) · Journal of Molecular Diagnostics · doi:10.2353/jmoldx.2006.050079 · Cited by 249 · see citing works

Association screening of common and rare genetic variants by penalized regression

Zhou, H., Sehl, M. E., Sinsheimer, J. S., & Lange, K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq448 · Cited by 137 · cited 1× · see citing works

Exact block-wise optimization in group lasso and sparse group lasso for linear regressionPreprint

Foygel, R., & Drton, M. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1010.3320 · Cited by 24 · see citing works

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752 · see citing works

The gut microbiome in atherosclerotic cardiovascular disease

Jie, Z., Xia, H., Zhong, S.-L., Feng, Q., Li, S., Liang, S., Zhong, H., Liu, Z., Gao, Y., Zhao, H., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00900-1 · Cited by 1,638 · see citing works

Machine learning advances for time series forecasting

Masini, R., Medeiros, M. C., & Mendes, E. F. (2021) · Journal of Economic Surveys · doi:10.1111/joes.12429 · Cited by 544 · see citing works

Group sparse regularization for deep neural networks

Scardapane, S., Comminiello, D., Hussain, A., & Uncini, A. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.029 · Cited by 455 · see citing works

Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?

Yi, S., Xu, Z., & Wang, G.-J. (2018) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.08.012 · Cited by 406 · see citing works

Neural Granger Causality

Tank, A., Covert, I., Foti, N. J., Shojaie, A., & Fox, E. B. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3065601 · Cited by 369 · see citing works

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Khalajmehrabadi, A., Gatsis, N., & Akopian, D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2671454 · Cited by 339 · see citing works

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Diao, J. A., Wang, J. K., Chui, W. F., Mountain, V., Gullapally, S. C., Srinivasan, R., Mitchell, R. N., Glass, B., Hoffman, S., Rao, S. K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21896-9 · Cited by 281 · see citing works

Explaining domestic energy consumption – The comparative contribution of building factors, socio-demographics, behaviours and attitudes

Huebner, G. M., Hamilton, I., Chalabi, Z., Shipworth, D., & Oreszczyn, T. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.09.028 · Cited by 279 · see citing works

Learning Interactions via Hierarchical Group-Lasso Regularization

Lim, M., & Hastie, T. J. (2014) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.938812 · Cited by 276 · see citing works

Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification

Sun, L., Zhang, X., Qian, Y., Xu, J., & Zhang, S. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.072 · Cited by 265 · see citing works

The Exposome: Molecules to Populations

Niedzwiecki, M. M., Walker, D. I., Vermeulen, R. C. H., Chadeau‐Hyam, M., Jones, D. P., & Miller, G. W. (2018) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010818-021315 · Cited by 252 · see citing works

Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes

Huebner, G. M., Shipworth, D., Hamilton, I., Chalabi, Z., & Oreszczyn, T. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.075 · Cited by 251 · see citing works

Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis

Zhao, Z., Wu, S., Qiao, B., Wang, S., & Chen, X. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2838070 · Cited by 234 · see citing works

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Emmert‐Streib, F., & Dehmer, M. (2019) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010021 · Cited by 217 · see citing works

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Xia, Y. (2020) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2020.04.003 · Cited by 217 · see citing works

Feature Selection for Neural Networks Using Group Lasso Regularization

Zhang, H., Wang, J., Sun, Z., Żurada, J. M., & Pal, N. R. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2893266 · Cited by 214 · see citing works

Vessels fuel consumption forecast and trim optimisation: A data analytics perspective

Coraddu, A., Oneto, L., Baldi, F., & Anguita, D. (2016) · Ocean Engineering · doi:10.1016/j.oceaneng.2016.11.058 · Cited by 210 · see citing works

VARX-L: Structured regularization for large vector autoregressions with exogenous variables

Nicholson, W. B., Matteson, D. S., & Bien, J. (2017) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2017.01.003 · Cited by 202 · see citing works

ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting

Ding, X., Hao, T., Tan, J., Liu, J., Han, J., Guo, Y., & Ding, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00447 · Cited by 200 · see citing works

A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease

de Vos, F., Koini, M., Schouten, T. M., Seiler, S., van der Grond, J., Lechner, A., Schmidt, R., de Rooij, M., & Rombouts, S. A. R. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.025 · Cited by 191 · see citing works

Machine-learning-based patient-specific prediction models for knee osteoarthritis

Jamshidi, A., Pelletier, J.-P., & Martel-Pelletier, J. (2019) · Nature Reviews Rheumatology · doi:10.1038/s41584-018-0130-5 · Cited by 190 · see citing works

Interpretable and explainable machine learning: A methods‐centric overview with concrete examples

Marcinkevičs, R., & Vogt, J. E. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1493 · Cited by 180 · see citing works

A Survey on Sparse Learning Models for Feature Selection

Li, X., Wang, Y., & Ruíz, R. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2982445 · Cited by 179 · see citing works

Machine Learning Time Series Regressions With an Application to Nowcasting

Babii, A., Ghysels, É., & Striaukas, J. (2021) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2021.1899933 · Cited by 177 · see citing works

Bayesian Variable Selection and Estimation for Group Lasso

Xu, X., & Ghosh, M. (2015) · Bayesian Analysis · doi:10.1214/14-ba929 · Cited by 172 · see citing works

Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

Zhang, W., Ota, T., Shridhar, V., CHIEN, J. R., Wu, B., & Kuang, R. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002975 · Cited by 172 · see citing works

Lecture notes on ridge regressionPreprint

van Wieringen (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.09169 · Cited by 164 · see citing works

Deep Optics for Single-Shot High-Dynamic-Range Imaging

Metzler, C. A., Ikoma, H., Peng, Y., & Wetzstein, G. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00145 · Cited by 161 · see citing works

Sparse Principal Component Analysis via Variable Projection

Erichson, N. B., Zheng, P., Manohar, K., Brunton, S. L., Kutz, J. N., & Aravkin, A. Y. (2020) · SIAM Journal on Applied Mathematics · doi:10.1137/18m1211350 · Cited by 158 · see citing works

Correlated variables in regression: Clustering and sparse estimation

Bühlmann, P., Rütimann, P., van de Geer, S., & Zhang, C. (2013) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2013.05.019 · Cited by 155 · see citing works

Learning Granger Causality for Hawkes ProcessesPreprint

Xu, H., Farajtabar, M., & Hongyuan Zha, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04511 · Cited by 155 · see citing works

Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems

Sun, L., Wang, L., Ding, W., Qian, Y., & Xu, J. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105373 · Cited by 143 · see citing works

Regularity and Conformity

Wang, Y., Yuan, N. J., Lian, D., Xu, L., Xie, X., Chen, E., & Rui, Y. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783350 · Cited by 139 · see citing works

Compression-aware Training of Deep NetworksPreprint

Álvarez, J. M., & Salzmann, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02638 · Cited by 136 · see citing works

Multivariate Sparse Group Lasso for the Multivariate Multiple Linear Regression with an Arbitrary Group Structure

Li, Y., Nan, B., & Zhu, J. (2015) · Biometrics · doi:10.1111/biom.12292 · Cited by 131 · see citing works

Large-scale benchmark study of survival prediction methods using multi-omics data

Herrmann, M., Probst, P., Hornung, R., Jurinović, V., & Boulesteix, A. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa167 · Cited by 131 · see citing works

Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions

Wang, G., Chen, Y., Si, H.-B., Xie, C., & Chevallier, J. (2021) · International Review of Economics & Finance · doi:10.1016/j.iref.2021.01.005 · Cited by 130 · see citing works

Sparse group lasso and high dimensional multinomial classification

Vincent, M., & Hansen, N. R. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.06.004 · Cited by 129 · see citing works

Preoperative factors and 3-year weight change in the Longitudinal Assessment of Bariatric Surgery (LABS) consortium

Courcoulas, A. P., Christian, N. J., O’Rourke, R. W., Dakin, G. F., Dellinger, E. P., Flum, D. R., Kalarchian, P. D. M., Mitchell, J. E., Patterson, E., Pomp, A., et al. (2015) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2015.01.011 · Cited by 126 · see citing works

NeuralSim: Augmenting Differentiable Simulators with Neural Networks

Heiden, E., Millard, D., Coumans, E., Sheng, Y., & Sukhatme, G. S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560935 · Cited by 122 · see citing works

A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image Classification

Song, L., Liu, J., Qian, B., Sun, M., Yang, K., Sun, M., & Abbas, S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2864920 · Cited by 116 · see citing works

Sparse Sliced Inverse Regression via Lasso

Lin, Q., Zhao, Z., & Liu, J. S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1520115 · Cited by 115 · see citing works

Transformed ℓ1 regularization for learning sparse deep neural networks

Ma, R., Miao, J., Niu, L., & Zhang, P. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.08.015 · Cited by 115 · see citing works

Volatility connectedness in global foreign exchange markets

Wen, T., & Wang, G. (2020) · Journal of Multinational Financial Management · doi:10.1016/j.mulfin.2020.100617 · Cited by 112 · see citing works

Feature-space selection with banded ridge regression

la Tour, T. D., Eickenberg, M., Núñez-Elizalde, A. O., & Gallant, J. L. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119728 · Cited by 111 · see citing works

Multi-label classification with weighted classifier selection and stacked ensemble

Xia, Y., Chen, K., & Yang, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.06.017 · Cited by 110 · see citing works

Group sparse canonical correlation analysis for genomic data integration

Lin, D., Zhang, J., Li, J., Calhoun, V. D., Deng, H., & Wang, Y. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-245 · Cited by 109 · see citing works

Hyperspectral Image Unmixing With Endmember Bundles and Group Sparsity Inducing Mixed Norms

Drumetz, L., Meyer, T. R., Chanussot, J., Bertozzi, A. L., & Jutten, C. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2897254 · Cited by 104 · see citing works

Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features

Xie, G., Dong, C., Kong, Y., Zhong, J. F., Li, M., & Wang, K. (2019) · Genes · doi:10.3390/genes10030240 · Cited by 102 · see citing works

Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study

Aycheh, H. M., Seong, J., Shin, J., Na, D. L., Kang, B., Seo, S. W., & Sohn, K.-A. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00252 · Cited by 102 · see citing works

Logistic Regression: From Art to Science

Bertsimas, D., & King, A. (2017) · Statistical Science · doi:10.1214/16-sts602 · Cited by 100 · see citing works

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Hsu, D. (2015) · Energy · doi:10.1016/j.energy.2015.02.008 · Cited by 99 · see citing works

A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method

Wang, J., Xu, C., Xi-feng, Y., & Żurada, J. M. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2748585 · Cited by 99 · see citing works

Connectivity strength‐weighted sparse group representation‐based brain network construction for M CI classification

Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., & Shen, D. G. (2017) · Human Brain Mapping · doi:10.1002/hbm.23524 · Cited by 98 · see citing works

IPF-LASSO: Integrative L 1 -Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

Boulesteix, A., Bin, R. D., Jiang, X., & Fuchs, M. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/7691937 · Cited by 96 · see citing works

Context-Aware Surveillance Video Summarization

Zhang, S., Zhu, Y., & Roy–Chowdhury, A. K. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2601493 · Cited by 96 · see citing works

A tutorial-based survey on feature selection: Recent advancements on feature selection

Moslemi, A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107136 · Cited by 96 · see citing works

The group exponential lasso for bi‐level variable selection

Breheny, P. J. (2015) · Biometrics · doi:10.1111/biom.12300 · Cited by 95 · see citing works

Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers

Aung, M. T., Yu, Y., Ferguson, K. K., Cantonwine, D. E., Zeng, L., McElrath, T. F., Pennathur, S., Mukherjee, B., & Meeker, J. D. (2019) · Scientific Reports · doi:10.1038/s41598-019-53448-z · Cited by 93 · see citing works

Building the graph of medicine from millions of clinical narratives

Finlayson, S. G., LePendu, P., & Shah, N. H. (2014) · Scientific Data · doi:10.1038/sdata.2014.32 · Cited by 91 · see citing works

Estimation of Broadband Multiuser Millimeter Wave Massive MIMO-OFDM Channels by Exploiting Their Sparse Structure

Lin, X., Wu, S. Y., Jiang, C., Kuang, L., Yan, J., & Hanzo, L. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2818142 · Cited by 90 · see citing works

An Introduction to Compressed Sensing

Vidyasagar, M. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976120 · Cited by 89 · see citing works

Discovery of sparse, reliable omic biomarkers with Stabl

Hedou, J. J., Marić, I., Bellan, G., Einhaus, J. F., Gaudilliere, D. K., Ladant, F.-X., Verdonk, F., Stelzer, I. A., Feyaerts, D., Tsai, A. S., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02033-x · Cited by 89 · see citing works

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Culley, C., Vijayakumar, S., Zampieri, G., & Angione, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002959117 · Cited by 88 · see citing works

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data

Lourenço, V., Ogutu, J. O., Rodrigues, R. A. P., Posekany, A., & Piepho, H. (2024) · BMC Genomics · doi:10.1186/s12864-023-09933-x · Cited by 86 · see citing works

Identifying Predictive Features in Drug Response Using Machine Learning: Opportunities and Challenges

Vidyasagar, M. (2014) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010814-124502 · Cited by 86 · see citing works

Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition

Kim, E. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2972628 · Cited by 85 · see citing works

Learning Graphical Models With Hubs

Tan, K. M., London, P., Mohan, K., Lee, S., Fazel, M., & Witten, D. (2014) · PubMed · doi:10.48550/arxiv.1402.7349 · Cited by 84 · see citing works

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data

Prince, J., Andreotti, F., & Vos, M. D. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2873252 · Cited by 83 · see citing works

Neural architecture search: A contemporary literature review for computer vision applications

Poyser, M., & Breckon, T. P. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110052 · Cited by 82 · see citing works

Classification With the Sparse Group Lasso

Rao, N., Nowak, R. D., Cox, C. R., & Rogers, T. T. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2488586 · Cited by 82 · see citing works

Towards Model Compression for Deep Learning Based Speech Enhancement

Tan, K., & Wang, D. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3082282 · Cited by 81 · see citing works

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging

Schouten, T. M., Koini, M., de Vos, F., Seiler, S., de Rooij, M., Lechner, A., Schmidt, R., van den Heuvel, M. P., van der Grond, J., & Rombouts, S. A. R. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.025 · Cited by 80 · see citing works

The impact of COVID-19 pandemic on the volatility connectedness network of global stock market

Cheng, T., Liu, J., Yao, W., & Zhao, A. B. (2021) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2021.101678 · Cited by 80 · see citing works

Subspace Sparse Discriminative Feature Selection

Nie, F., Wang, Z., Tian, L., Wang, R., & Li, X. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3025205 · Cited by 79 · see citing works

Efficient gradient boosting for prognostic biomarker discovery

Li, K., Yao, S., Zhang, Z., Cao, B., Wilson, C. M., Kalos, D., Kuan, P. F., Zhu, R., & Wang, X. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab869 · Cited by 76 · see citing works

Weighted General Group Lasso for Gene Selection in Cancer Classification

Wang, Y., Li, X., & Ruíz, R. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2829811 · Cited by 73 · see citing works

Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression

Ammad-ud-din, M., Khan, S. A., Wennerberg, K., & Aittokallio, T. A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx266 · Cited by 73 · see citing works

Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data

Bastien, P., Bertrand, F., Meyer, N., & Maumy‐Bertrand, M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu660 · Cited by 72 · see citing works

Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and ClassificationPreprint

Feng, J., & Simon, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07592 · Cited by 70 · see citing works

A Nearly-Linear Time Framework for Graph-Structured Sparsity

Hegde, C., Indyk, P., & Schmidt, L. (2015) · Cited by 68 · see citing works

An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems

Zhang, Y., Zhang, N., Sun, D., & Toh, K.-C. (2018) · Mathematical Programming · doi:10.1007/s10107-018-1329-6 · Cited by 68 · see citing works

Grouped Gene Selection of Cancer via Adaptive Sparse Group Lasso Based on Conditional Mutual Information

Li, J., Dong, W., & Meng, D. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2761871 · Cited by 67 · see citing works

A toolbox for brain network construction and classification (BrainNetClass)

Zhou, Z., Chen, X., Zhang, Y., Hu, D., Qiao, L., Yu, R., Yap, P., Pan, G., Zhang, H., & Shen, D. G. (2020) · Human Brain Mapping · doi:10.1002/hbm.24979 · Cited by 67 · see citing works

SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso

Huo, Y., Xin, L., Kang, C., Wang, M., Ma, Q., & Yu, B. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.110098 · Cited by 66 · see citing works

Sparse fully convolutional network for face labeling

Dong, M., Wen, S., Zeng, Z., Yan, Z., & Huang, T. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.079 · Cited by 65 · see citing works

Regularized regression for categorical data

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Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

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