Regularization and Variable Selection Via the Elastic Net
Open accessAbstract
Summary: We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p≫n case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
When there are disproportionately few samples relative to the number of features, or when some input features are sparsely represented, early or late fusion is preferred because they can be implemented with traditional machine learning algorithms better suited to such data.
“For example, if there are disproportionately few samples relative to the number of features in the dataset or if some of the input features are sparsely represented, early or late fusion is preferred because they can be implemented with traditional machine learning algorithms (e.g., Lasso and ElasticNet47) that are better suited for this type of data48.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [47]
The elastic net algorithm was used because penalization improves prediction accuracy and maintains clinical interpretability compared with less transparent machine learning algorithms.
“We used the elastic net algorithm, given substantial evidence that penalization improves prediction accuracy,25–27 alongside its well-established accuracy and robustness28,29 and ability to maintain clinical interpretability compared with less transparent machine learning algorithms (eg, random forests, neural networks).”
From A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior (2021) · cites this paper as [29]
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Self-citationVariable Selection via Gibbs Sampling
George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828 · see citing works
'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P. (2000) · Genome Biology · doi:10.1186/gb-2000-1-2-research0003 · Cited by 547 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · see citing works
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270 · see citing works
Parikh, N., & Boyd, S. (2014) · Foundations and Trends® in Optimization · doi:10.1561/2400000003 · Cited by 4,102 · see citing works
IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., Albright, A., Cheng, J. D., Kang, S. P., Shankaran, V., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci91190 · Cited by 3,788 · see citing works
Gradient boosting machines, a tutorial
Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887 · see citing works
Sparse Principal Component Analysis
Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152 · see citing works
Self-citationData-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228 · see citing works
Detection and localization of surgically resectable cancers with a multi-analyte blood test
Cohen, J. D., Li, L., Wang, Y., Thoburn, C., Afsari, B., Danilova, L., Douville, C., Javed, A. A., Wong, F., Mattox, A., et al. (2018) · Science · doi:10.1126/science.aar3247 · Cited by 2,783 · see citing works
Calculating the sample size required for developing a clinical prediction model
Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020) · BMJ · doi:10.1136/bmj.m441 · Cited by 2,917 · see citing works
Systematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456 · see citing works
Strobl, C., Malley, J., & Tutz, G. (2009) · Psychological Methods · doi:10.1037/a0016973 · Cited by 2,583 · see citing works
DNA methylation GrimAge strongly predicts lifespan and healthspan
Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., Hou, L., Baccarelli, A. A., Li, Y., Stewart, J. D., et al. (2019) · Aging · doi:10.18632/aging.101684 · Cited by 2,540 · see citing works
Accelerating extinction risk from climate change
Urban, M. C. (2015) · Science · doi:10.1126/science.aaa4984 · Cited by 2,436 · see citing works
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works
Fake news on Twitter during the 2016 U.S. presidential election
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019) · Science · doi:10.1126/science.aau2706 · Cited by 1,803 · see citing works
Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study
Kuderer, N. M., Choueiri, T. K., Shah, D. P., Shyr, Y., Rubinstein, S. M., Rivera, D. R., Shete, S., Hsu, C.-Y., Desai, A., de Lima Lopes, G., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31187-9 · Cited by 1,726 · see citing works
Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2013) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2012.681250 · Cited by 1,380 · see citing works
Self-citationRelief-based feature selection: Introduction and review
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.014 · Cited by 1,311 · see citing works
Intertumoral Heterogeneity within Medulloblastoma Subgroups
Cavalli, F. M. G., Remke, M., Rampasek, L., Peacock, J., Shih, D. J. H., Luu, B., Garzia, L., Torchia, J., Nor, C., Morrissy, A. S., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.05.005 · Cited by 1,219 · see citing works
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., et al. (2020) · Nature · doi:10.1038/s41586-020-1994-5 · Cited by 1,180 · see citing works
A Validated Regulatory Network for Th17 Cell Specification
Ciofani, M., Madar, A., Galan, C., Sellars, M., Mace, K., Pauli, F., Agarwal, A., Huang, W., Parkurst, C. N., Muratet, M., et al. (2012) · Cell · doi:10.1016/j.cell.2012.09.016 · Cited by 1,160 · see citing works
Genetic and functional drivers of diffuse large B cell lymphoma
Reddy, A., Zhang, J., Davis, N. S., Moffitt, A. B., Love, C. L., Waldrop, A., Leppa, S., Pasanen, A., Meriranta, L., Karjalainen-Lindsberg, M.-L., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.027 · Cited by 1,104 · see citing works
Machine Learning Methods That Economists Should Know About
Athey, S., & Imbens, G. W. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053433 · Cited by 1,163 · see citing works
A simple new approach to variable selection in regression, with application to genetic fine mapping
Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12388 · Cited by 1,121 · see citing works
Deep learning-based multi-omics integration robustly predicts survival in liver cancer
Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0853 · Cited by 1,091 · see citing works
Predicting reaction performance in C–N cross-coupling using machine learning
Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G. (2018) · Science · doi:10.1126/science.aar5169 · Cited by 1,058 · see citing works
Kootte, R. S., Levin, E., Salojärvi, J., Smits, L. P., Hartstra, A. V., Udayappan, S. D., Hermes, G., Bouter, K. E., Koopen, A. M., Holst, J. J., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.09.008 · Cited by 1,039 · see citing works
Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063 · see citing works
Principles of connectivity among morphologically defined cell types in adult neocortex
Jiang, X., Shen, S., Cadwell, C. R., Berens, P., Sinz, F., Ecker, A. S., Patel, S., & Tolias, A. S. (2015) · Science · doi:10.1126/science.aac9462 · Cited by 908 · see citing works
Horlbeck, M. A., Gilbert, L. A., Villalta, J. E., Adamson, B., Pak, R. A., Chen, Y., Fields, A. P., Park, C. Y., Corn, J. E., Kampmann, M., et al. (2016) · eLife · doi:10.7554/elife.19760 · Cited by 894 · see citing works
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017) · Computers & Education · doi:10.1016/j.compedu.2016.10.001 · Cited by 1,003 · see citing works
Ranstam, J., & Cook, J. A. (2018) · British Journal of Surgery · doi:10.1002/bjs.10895 · Cited by 1,000 · see citing works
Undulating changes in human plasma proteome profiles across the lifespan
Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., Moran Losada, P., Berdnik, D., Keller, A., Verghese, J., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0673-2 · Cited by 981 · see citing works
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912 · see citing works
Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data
Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.044 · Cited by 789 · see citing works
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · see citing works
Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · see citing works
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works
Neural Network Methods for Natural Language Processing
Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821 · see citing works
Horvath, S., Oshima, J., Martin, G. M., Lu, A. T., Quach, A., Cohen, H., Felton, S., Matsuyama, M., Lowe, D., Kabacik, S., et al. (2018) · Aging · doi:10.18632/aging.101508 · Cited by 815 · see citing works
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4273 · Cited by 782 · see citing works
Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease
Brier, M. R., Gordon, B., Friedrichsen, K., McCarthy, J., Stern, A., Christensen, J., Owen, C., Aldea, P., Su, Y., Hassenstab, J., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf2362 · Cited by 734 · see citing works
Bias in Cable News: Persuasion and Polarization
Martin, G. J., & Yurukoglu, A. (2017) · American Economic Review · doi:10.1257/aer.20160812 · Cited by 666 · see citing works
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00471-x · Cited by 676 · see citing works
Integrated omics: tools, advances and future approaches
Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019) · Journal of Molecular Endocrinology · doi:10.1530/jme-18-0055 · Cited by 686 · see citing works
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004977 · Cited by 659 · see citing works
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651 · see citing works
Characterizing a psychiatric symptom dimension related to deficits in goal-directed control
Gillan, C. M., Kosinski, M., Whelan, R., Phelps, E. A., & Daw, N. D. (2016) · eLife · doi:10.7554/elife.11305 · Cited by 586 · see citing works
Belsky, D. W., Caspi, A., Arseneault, L., Baccarelli, A., Corcoran, D. L., Gao, X., Hannon, E., Harrington, H. L., Rasmussen, L. J., Houts, R., et al. (2020) · eLife · doi:10.7554/elife.54870 · Cited by 652 · see citing works
Lago, J., De Ridder, F., & De Schutter, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.069 · Cited by 642 · see citing works
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637 · see citing works
Sequence determinants of improved CRISPR sgRNA design
Xu, H., Xiao, T., Chen, C.-H., Li, W., Meyer, C. A., Wu, Q., Wu, D., Cong, L., Zhang, F., Liu, J. S., et al. (2015) · Genome Research · doi:10.1101/gr.191452.115 · Cited by 625 · see citing works
A targeted real-time early warning score (TREWScore) for septic shock.
Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aab3719 · Cited by 598 · see citing works
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622 · see citing works
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677 · see citing works
Modeling causes of death: an integrated approach using CODEm
Foreman, K. J., Lozano, R., Lopez, A. D., & Murray, C. J. (2012) · Population Health Metrics · doi:10.1186/1478-7954-10-1 · Cited by 564 · see citing works
A Review of Feature Reduction Techniques in Neuroimaging
Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559 · see citing works
Sayed, N., Huang, Y., Nguyen, K., Krejciova-Rajaniemi, Z., Grawe, A. P., Gao, T., Tibshirani, R., Hastie, T., Alpert, A., Cui, L., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00082-y · Cited by 572 · see citing works
Self-citationImage super-resolution: the techniques, applications, and future
Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., & Zhang, L. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.05.002 · Cited by 554 · see citing works
Measuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570 · see citing works
The psychological impact of the COVID-19 outbreak on health professionals: A cross-sectional study
Giusti, E. M., Pedroli, E., D'Aniello, G. E., Stramba Badiale, C., Pietrabissa, G., Manna, C., Stramba Badiale, M., Riva, G., Castelnuovo, G., & Molinari, E. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01684 · Cited by 533 · see citing works
A significance test for the lasso
Lockhart, R., Taylor, J., Tibshirani, R. J., & Tibshirani, R. (2014) · The Annals of Statistics · doi:10.1214/13-aos1175 · Cited by 541 · see citing works
Motzer, R. J., Robbins, P. B., Powles, T., Albiges, L., Haanen, J. B., Larkin, J., Mu, X. J., Ching, K. A., Uemura, M., Pal, S. K., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1044-8 · Cited by 518 · see citing works
Plasma protein patterns as comprehensive indicators of health
Williams, S. A., Kivimaki, M., Langenberg, C., Hingorani, A. D., Casas, J. P., Bouchard, C., Jonasson, C., Sarzynski, M. A., Shipley, M. J., Alexander, L., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0665-2 · Cited by 506 · see citing works
Supervised classification enables rapid annotation of cell atlases
Pliner, H. A., Shendure, J., & Trapnell, C. (2019) · doi:10.1101/538652 · Cited by 498 · see citing works
Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses
Amit, I., Garber, M., Chevrier, N., Leite, A. P., Donner, Y., Eisenhaure, T., Guttman, M., Grenier, J. K., Li, W., Zuk, O., et al. (2009) · Science · doi:10.1126/science.1179050 · Cited by 488 · see citing works
Zhang, Q., Vallerga, C. L., Walker, R. M., Lin, T., Henders, A. K., Montgomery, G. W., He, J., Fan, D., Fowdar, J., Kennedy, M., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0667-1 · Cited by 487 · see citing works
Fast nonnegative deconvolution for spike train inference from population calcium imaging
Vogelstein, J. T., Packer, A. M., Machado, T. A., Sippy, T., Babadi, B., Yuste, R., & Paninski, L. (2010) · Journal of Neurophysiology · doi:10.1152/jn.01073.2009 · Cited by 449 · see citing works
Kessler, R. C., Warner, C. H., Ivany, C., Petukhova, M. V., Rose, S., Bromet, E. J., Brown, M., Cai, T., Colpe, L. J., Cox, K. L., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1754 · Cited by 447 · see citing works
Treatment selection in depression.
Cohen, Z. D., & DeRubeis, R. J. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050817-084746 · Cited by 435 · see citing works
Ogutu, J. O., Schulz-Streeck, T., & Piepho, H.-P. (2012) · BMC Proceedings · doi:10.1186/1753-6561-6-s2-s10 · Cited by 440 · see citing works
Towards a rigorous assessment of systems biology models: the DREAM3 challenges
Prill, R. J., Marbach, D., Saez-Rodriguez, J., Sorger, P. K., Alexopoulos, L. G., Xue, X., Clarke, N. D., Altan-Bonnet, G., & Stolovitzky, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009202 · Cited by 439 · see citing works
DNA methylation-based estimator of telomere length
Lu, A. T., Seeboth, A., Tsai, P.-C., Sun, D., Quach, A., Reiner, A. P., Kooperberg, C., Ferrucci, L., Hou, L., Baccarelli, A. A., et al. (2019) · Aging · doi:10.18632/aging.102173 · Cited by 440 · see citing works
Heslot, N., Akdemir, D., Sorrells, M. E., & Jannink, J.-L. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2231-5 · Cited by 409 · see citing works
Recent advances in feature selection and its applications
Li, Y., Li, T., & Liu, H. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1059-8 · Cited by 400 · see citing works
Diagnosis of Childhood Tuberculosis and Host RNA Expression in Africa
Anderson, S. T., Kaforou, M., Brent, A. J., Wright, V. J., Banwell, C. M., Chagaluka, G., Crampin, A. C., Dockrell, H. M., French, N., Hamilton, M. S., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1303657 · Cited by 379 · see citing works
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380 · see citing works
Hirayama, A. V., Gauthier, J., Hay, K. A., Voutsinas, J. M., Wu, Q., Gooley, T., Li, D., Cherian, S., Chen, X., Pender, B. S., et al. (2019) · Blood · doi:10.1182/blood-2018-11-887067 · Cited by 355 · see citing works
Advances and opportunities in machine learning for process data analytics
Qin, S. J., & Chiang, L. H. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.003 · Cited by 351 · see citing works
Cytomegalovirus infection enhances the immune response to influenza
Furman, D., Jojic, V., Sharma, S., Shen-Orr, S. S., L. Angel, C. J., Onengut-Gumuscu, S., Kidd, B. A., Maecker, H. T., Concannon, P., Dekker, C. L., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa2293 · Cited by 332 · see citing works
A small number of abnormal brain connections predicts adult autism spectrum disorder
Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., et al. (2016) · Nature Communications · doi:10.1038/ncomms11254 · Cited by 305 · see citing works
Big Data in Public Health: Terminology, Machine Learning, and Privacy
Mooney, S. J., & Pejaver, V. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014208 · Cited by 325 · see citing works
Global Proteome Analysis of the NCI-60 Cell Line Panel
Gholami, A. M., Hahne, H., Wu, Z., Auer, F. J., Meng, C., Wilhelm, M., & Kuster, B. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.07.018 · Cited by 302 · see citing works
Chen, L., Zhernakova, D. V., Kurilshikov, A., Andreu-Sánchez, S., Wang, D., Augustijn, H. E., Vich Vila, A., Study, L. C., Weersma, R. K., Medema, M. H., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02014-8 · Cited by 327 · see citing works
Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319 · see citing works
Social status alters immune regulation and response to infection in macaques
Snyder-Mackler, N., Sanz, J., Kohn, J. N., Brinkworth, J. F., Morrow, S., Shaver, A. O., Grenier, J.-C., Pique-Regi, R., Johnson, Z. P., Wilson, M. E., et al. (2016) · Science · doi:10.1126/science.aah3580 · Cited by 318 · see citing works
Patsopoulos, N. A., Barcellos, L. F., Hintzen, R. Q., Schaefer, C., van Duijn, C. M., Noble, J. A., Raj, T., IMSGC, ANZgene, Gourraud, P.-A., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003926 · Cited by 312 · see citing works
Calibration drift in regression and machine learning models for acute kidney injury
Davis, S. E., Lasko, T. A., Chen, G., Siew, E. D., & Matheny, M. E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx030 · Cited by 312 · see citing works
Machine learning for renewable energy materials
Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311 · see citing works
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Wainer, J., & Cawley, G. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115222 · Cited by 309 · see citing works
Making sense of the ageing methylome
Seale, K., Horvath, S., Teschendorff, A., Eynon, N., & Voisin, S. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00477-6 · Cited by 274 · see citing works
Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289 · see citing works
Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials
Lipkovich, I., Dmitrienko, A., & B., R. (2017) · Statistics in Medicine · doi:10.1002/sim.7064 · Cited by 291 · see citing works
Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E. (2014) · Genes, Brain and Behavior · doi:10.1111/gbb.12106 · Cited by 289 · see citing works
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2021) · doi:10.1101/2021.10.05.463203 · Cited by 284
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01048-5 · Cited by 283 · see citing works
Fernández, R. S., Crivelli, L., Guimet, N. M., Allegri, R. F., & Pedreira, M. E. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.07.133 · Cited by 245 · see citing works
A Survey of Knowledge-enhanced Text Generation
Yu, W., Zhu, C., Li, Z., Hu, Z., Wang, Q., Ji, H., & Jiang, M. (2022) · ACM Computing Surveys · doi:10.1145/3512467 · Cited by 257 · see citing works
- Authors
- Zou, H., & Hastie, T.
- Affiliations
- Stanford University (US)
- Corresponding author
- Hastie, Trevor
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-03-09
- Volume
- 67
- Issue
- 2
- Pages
- 301-320
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2122825543
- Cited by
- 20,957
- References
- 5
- Citations by year
- 2026: 1933 · 2025: 1906 · 2024: 1653 · 2023: 1540 · 2022: 1664 · 2021: 1924 · 2020: 1702 · 2019: 1536 · 2018: 1338 · 2017: 1092 · 2016: 949 · 2015: 884 · 2014: 727 · 2013: 585 · 2012: 468
- FWCI
- 79.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (DMS-0204162)
- National Institutes of Health (RO1-EB0011988-08)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Keywords
- elastic net, regularization, feature selection, LASSO, high-dimensional data, correlated predictors, grouping effect, regularization path, sparse representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
