{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T21:51:09Z","timestamp":1791323469262,"version":"4.3.1"},"reference-count":65,"publisher":"Institute of Mathematical Statistics","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ann. Appl. Stat."],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:p>Predicting drug sensitivity and selecting informative biomarkers are fundamental challenges in precision oncology. These tasks are complicated by the ultrahigh-dimensional nature of omics data, where the number of covariates p far exceeds the sample size n. While variable selection methods are widely used, traditional high-dimensional regression techniques often rely on the assumption of Gaussian noise, which is frequently violated in real-world omics datasets. To overcome these limitations, we propose a Bayesian empirical likelihood approach (BEL_HD) for variable selection and prediction in a semiparametric framework. BEL_HD avoids strict distributional assumptions by utilizing estimating equations and imposes joint regularization on both regression coefficients and the Lagrange multipliers to promote sparsity. We also introduce an efficient active set MCMC algorithm that enables scalable inference in ultrahigh-dimensional spaces. Through simulations and real-data applications, including a case study on leukemia drug sensitivity, BEL_HD demonstrates superior predictive performance and selects biologically interpretable features. Our method combines the flexibility of empirical likelihood with the inferential benefits of Bayesian modeling, offering a robust and extensible tool for high-dimensional biomedical research.<\/jats:p>","DOI":"10.1214\/26-aoas2210","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:33:01Z","timestamp":1790609581000},"page":"2364-2385","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian empirical likelihood for UltraHigh-dimensional variable selection with application to drug sensitivity"],"prefix":"10.1214","volume":"20","author":[{"given":"Can","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, Southern Methodist University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4272-8388","authenticated-orcid":false,"given":"Zeyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Division of Data Science, University of Texas at Arlington"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute for Insight, Georgia State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifan","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Statistics, Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Georgia State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8561-6511","authenticated-orcid":false,"given":"Xinlei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Division of Data Science, University of Texas at Arlington"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"108","reference":[{"key":"25","doi-asserted-by":"publisher","unstructured":"<small>Kitamura, Y.<\/small> (1997). Empirical likelihood methods with weakly dependent processes. <i>Ann. Statist.<\/i> <b>25<\/b> 2084\u20132102.","DOI":"10.1214\/aos\/1069362388"},{"key":"38","doi-asserted-by":"publisher","unstructured":"<small>Owen, A. B.<\/small> (1988). Empirical likelihood ratio confidence intervals for a single functional. <i>Biometrika<\/i> <b>75<\/b> 237\u2013249.","DOI":"10.1093\/biomet\/75.2.237"},{"key":"31","doi-asserted-by":"publisher","unstructured":"<small>MAZUMDER, R.<\/small>, <small>FRIEDMAN, J. H.<\/small> and <small>HASTIE, T.<\/small> (2011). SparseNet: Coordinate descent with nonconvex penalties. <i>J. Amer. Statist. Assoc.<\/i> <b>106<\/b> 1125\u20131138.","DOI":"10.1198\/jasa.2011.tm09738"},{"key":"19","doi-asserted-by":"publisher","unstructured":"<small>George, E. I.<\/small> and <small>McCulloch, R. E.<\/small> (1993). Variable selection via Gibbs sampling. <i>J. Amer. Statist. Assoc.<\/i> <b>88<\/b> 881\u2013889.","DOI":"10.2307\/2290777"},{"key":"16","doi-asserted-by":"publisher","unstructured":"<small>Fan, J.<\/small> and <small>Li, R.<\/small> (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. <i>J. Amer. Statist. Assoc.<\/i> <b>96<\/b> 1348\u20131360.","DOI":"10.1198\/016214501753382273"},{"key":"62","doi-asserted-by":"publisher","unstructured":"<small>Zhang, C.-H.<\/small> (2010). Nearly unbiased variable selection under minimax concave penalty. <i>Ann. Statist.<\/i> <b>38<\/b> 894\u2013942.","DOI":"10.1214\/09-AOS729"},{"key":"65","doi-asserted-by":"publisher","unstructured":"<small>Zou, H.<\/small> (2006). The adaptive lasso and its oracle properties. <i>J. Amer. Statist. Assoc.<\/i> <b>101<\/b> 1418\u20131429.","DOI":"10.1198\/016214506000000735"},{"key":"41","doi-asserted-by":"publisher","unstructured":"<small>Park, T.<\/small> and <small>Casella, G.<\/small> (2008). The Bayesian lasso. <i>J. Amer. Statist. Assoc.<\/i> <b>103<\/b> 681\u2013686.","DOI":"10.1198\/016214508000000337"},{"key":"17","doi-asserted-by":"publisher","unstructured":"<small>Fan, J.<\/small> and <small>Lv, J.<\/small> (2008). Sure independence screening for ultrahigh dimensional feature space. <i>J. R. Stat. Soc. Ser. B. Stat. Methodol.<\/i> <b>70<\/b> 849\u2013911.","DOI":"10.1111\/j.1467-9868.2008.00674.x"},{"key":"24","doi-asserted-by":"publisher","unstructured":"<small>Johnson, V. E.<\/small> and <small>Rossell, D.<\/small> (2012). Bayesian model selection in high-dimensional settings. <i>J. Amer. Statist. Assoc.<\/i> <b>107<\/b> 649\u2013660.","DOI":"10.1080\/01621459.2012.682536"},{"key":"49","unstructured":"<small>Shin, M.<\/small>, <small>Bhattacharya, A.<\/small> and <small>Johnson, V. E.<\/small> (2018). Scalable Bayesian variable selection using nonlocal prior densities in ultrahigh-dimensional settings. <i>Statist. Sinica<\/i> <b>28<\/b> 1053\u20131078."},{"key":"36","doi-asserted-by":"publisher","unstructured":"<small>Owen, A.<\/small> (1990). Empirical likelihood ratio confidence regions. <i>Ann. Statist.<\/i> <b>18<\/b> 90\u2013120.","DOI":"10.1214\/aos\/1176347494"},{"key":"23","doi-asserted-by":"publisher","unstructured":"<small>Ishwaran, H.<\/small> and <small>Rao, J. S.<\/small> (2005). Spike and slab variable selection: Frequentist and Bayesian strategies. <i>Ann. Statist.<\/i> <b>33<\/b> 730\u2013773.","DOI":"10.1214\/009053604000001147"},{"key":"48","doi-asserted-by":"publisher","unstructured":"<small>Scott, J. G.<\/small> and <small>Berger, J. O.<\/small> (2010). Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem. <i>Ann. Statist.<\/i> <b>38<\/b> 2587\u20132619.","DOI":"10.1214\/10-AOS792"},{"key":"34","doi-asserted-by":"publisher","unstructured":"<small>MURPHY, S. A.<\/small> and <small>VAN DER VAART, A. W.<\/small> (1997). Semiparametric likelihood ratio inference. <i>Ann. Statist.<\/i> <b>25<\/b> 1471\u20131509.","DOI":"10.1214\/aos\/1031594729"},{"key":"37","doi-asserted-by":"publisher","unstructured":"<small>OWEN, A.<\/small> (1991). Empirical likelihood for linear models. <i>Ann. Statist.<\/i> <b>19<\/b> 1725\u20131747.","DOI":"10.1214\/aos\/1176348368"},{"key":"35","doi-asserted-by":"publisher","unstructured":"<small>NORDMAN, D. J.<\/small> (2009). Tapered empirical likelihood for time series data in time and frequency domains. <i>Biometrika<\/i> <b>96<\/b> 119\u2013132.","DOI":"10.1093\/biomet\/asn071"},{"key":"5","doi-asserted-by":"crossref","unstructured":"<small>CHEN, S. X.<\/small>, <small>PENG, L.<\/small> and <small>QIN, Y.-L.<\/small> (2009). Effects of data dimension on empirical likelihood. <i>Biometrika<\/i> <b>96<\/b> 711\u2013722.","DOI":"10.1093\/biomet\/asp037"},{"key":"52","doi-asserted-by":"crossref","unstructured":"<small>TIBSHIRANI, R.<\/small> (1996). Regression shrinkage and selection via the lasso. <i>J. Roy. Statist. Soc. Ser. B, Methodol.<\/i> <b>58<\/b> 267\u2013288.","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"39","unstructured":"<small>OWEN, A. B.<\/small> (2001). <i>Empirical Likelihood<\/i>. CRC press, Boca Raton."},{"key":"1","doi-asserted-by":"crossref","unstructured":"<small>ANDREWS, D. F.<\/small> and <small>MALLOWS, C. L.<\/small> (1974). Scale mixtures of normal distributions. <i>J. Roy. Statist. Soc. Ser. B, Methodol.<\/i> <b>36<\/b> 99\u2013102.","DOI":"10.1111\/j.2517-6161.1974.tb00989.x"},{"key":"2","doi-asserted-by":"publisher","unstructured":"<small>BEDOUI, A.<\/small> and <small>LAZAR, N. A.<\/small> (2020). Bayesian empirical likelihood for ridge and lasso regressions. <i>Comput. Statist. Data Anal.<\/i> <b>145<\/b> 106917, 15.","DOI":"10.1016\/j.csda.2020.106917"},{"key":"3","doi-asserted-by":"publisher","unstructured":"<small>CHANG, J.<\/small>, <small>TANG, C. Y.<\/small>, <small>WU, T. T.<\/small> et al. (2018). A new scope of penalized empirical likelihood with high-dimensional estimating equations. <i>Ann. Statist.<\/i> <b>46<\/b> 3185\u20133216.","DOI":"10.1214\/17-AOS1655"},{"key":"4","unstructured":"<small>CHAWLA, S.<\/small>, <small>ROCKSTROH, A.<\/small>, <small>LEHMAN, M.<\/small>, <small>RATTHER, E.<\/small>, <small>JAIN, A.<\/small>, <small>ANAND, A.<\/small>, <small>GUPTA, A.<\/small>, <small>BHATTACHARYA, N.<\/small>, <small>POONIA, S.<\/small> et al. (2022). Gene expression based inference of cancer drug sensitivity. <i>Nat. Commun.<\/i> <b>13<\/b> 5680."},{"key":"6","unstructured":"<small>CHEN, Y.<\/small>, <small>YIN, Z.<\/small>, <small>WESTOVER, K. D.<\/small>, <small>ZHOU, Z.<\/small> and <small>SHU, L.<\/small> (2025). Advances and challenges in RAS signaling targeted therapy in leukemia. <i>Mol. Cancer Ther.<\/i> <b>24<\/b> 33\u201346."},{"key":"7","doi-asserted-by":"publisher","unstructured":"<small>CHENG, Y.<\/small>, <small>DAI, J. Y.<\/small>, <small>WANG, X.<\/small> and <small>KOOPERBERG, C.<\/small> (2018). Identifying disease-associated copy number variations by a doubly penalized regression model. <i>Biometrics<\/i> <b>74<\/b> 1341\u20131350.","DOI":"10.1111\/biom.12920"},{"key":"8","doi-asserted-by":"publisher","unstructured":"<small>CHENG, Y.<\/small> and <small>ZHAO, Y.<\/small> (2026). Empirical likelihood based Bayesian variable selection. <i>Comput. Statist. Data Anal.<\/i> <b>213<\/b> Paper No. 108258, 13.","DOI":"10.1016\/j.csda.2025.108258"},{"key":"9","unstructured":"<small>CHUANG, C.-S.<\/small> and <small>CHAN, N. H.<\/small> (2002). Empirical likelihood for autoregressive models, with applications to unstable time series. <i>Statist. Sinica<\/i> <b>12<\/b> 387\u2013407."},{"key":"10","doi-asserted-by":"crossref","unstructured":"<small>CHURCH, B. V.<\/small>, <small>T., W. H.<\/small> and <small>MAR, J. C.<\/small> (2019). Investigating skewness to understand gene expression heterogeneity in large patient cohorts. <i>BMC Bioinform.<\/i> <b>20<\/b> 668.","DOI":"10.1186\/s12859-019-3252-0"},{"key":"11","doi-asserted-by":"publisher","unstructured":"<small>CONTICELLO, S. G.<\/small> (2008). The AID\/APOBEC family of nucleic acid mutators. <i>Genome Biol.<\/i> <b>9<\/b> 229.","DOI":"10.1186\/gb-2008-9-6-229"},{"key":"12","unstructured":"<small>DA COSTA, A.<\/small>, <small>CHOWDHURY, D.<\/small>, <small>SHAPIRO, G. I.<\/small>, <small>D\u2019ANDREA, A. D.<\/small> and <small>KONSTANTINOPOULOS, P. A.<\/small> (2023). Targeting replication stress in cancer therapy. <i>Nat. Rev. Drug Discov.<\/i> <b>22<\/b> 38\u201358."},{"key":"13","unstructured":"<small>DE MEL, S.<\/small>, <small>LEE, A. R.<\/small>, <small>TAN, J. H. I.<\/small>, <small>TAN, R. Z. Y.<\/small>, <small>POON, L. M.<\/small>, <small>CHAN, E.<\/small>, <small>LEE, J.<\/small>, <small>CHEE, Y. L.<\/small>, <small>LAKSHMINARASAPPA, S. R.<\/small> et al. (2024). Targeting the DNA damage response in hematological malignancies. <i>Front. Oncol.<\/i> <b>14<\/b> 1307839."},{"key":"14","doi-asserted-by":"crossref","unstructured":"<small>EHRLICH, I.<\/small> (1973). Participation in illegitimate activities: A theoretical and empirical investigation. <i>J. Polit. Econ.<\/i> <b>81<\/b> 521\u2013565.","DOI":"10.1086\/260058"},{"key":"15","doi-asserted-by":"publisher","unstructured":"<small>FAIVRE, S.<\/small>, <small>CHAN, D.<\/small>, <small>SALINAS, R.<\/small>, <small>WOYNAROWSKA, B.<\/small> and <small>WOYNAROWSKI, J. M.<\/small> (2003). DNA strand breaks and apoptosis induced by oxaliplatin in cancer cells. <i>Biochem. Pharmacol.<\/i> <b>66<\/b> 225\u201337.","DOI":"10.1016\/s0006-2952(03)00260-0"},{"key":"18","doi-asserted-by":"publisher","unstructured":"<small>FRIEDMAN, J.<\/small>, <small>HASTIE, T.<\/small>, <small>H\u00d6FLING, H.<\/small>, <small>TIBSHIRANI, R.<\/small> et al. (2007). Pathwise coordinate optimization. <i>Ann. Appl. Stat.<\/i> <b>1<\/b> 302\u2013332.","DOI":"10.1214\/07-AOAS131"},{"key":"20","doi-asserted-by":"publisher","unstructured":"<small>GEWEKE, J.<\/small> (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In <i>Bayesian Statistics<\/i> 4: <i>Proceedings of the Fourth Valencia International Meeting, Dedicated to the Memory of Morris H. DeGroot<\/i>, 1931<i>\u2013<\/i>1989 (J. M. Bernardo, J. O. Berger, P. Dawid, A. F. M. Smith, J. M. Bernardo, J. O. Berger, P. Dawid and A. F. M. Smith, eds.) Oxford University Press, London.","DOI":"10.1093\/oso\/9780198522669.003.0010"},{"key":"21","doi-asserted-by":"publisher","unstructured":"<small>HE, Y.<\/small>, <small>SUN, M. M.<\/small>, <small>ZHANG, G. G.<\/small>, <small>YANG, J.<\/small>, <small>CHEN, K. S.<\/small>, <small>XU, W. W.<\/small> and <small>LI, B.<\/small> (2021). Targeting PI3K\/Akt signal transduction for cancer therapy. <i>Signal Transduct. Targeted Ther.<\/i> <b>6<\/b> 425.","DOI":"10.1038\/s41392-021-00828-5"},{"key":"22","doi-asserted-by":"publisher","unstructured":"<small>HOSSAIN, M. N.<\/small>, <small>SAKEMURA, R.<\/small>, <small>FUJII, M.<\/small> and <small>AYUSAWA, D.<\/small> (2006). G-protein gamma subunit GNG11 strongly regulates cellular senescence. <i>Biochem. Biophys. Res. Commun.<\/i> <b>351<\/b> 645\u201350.","DOI":"10.1016\/j.bbrc.2006.10.112"},{"key":"26","doi-asserted-by":"publisher","unstructured":"<small>LAHIRI, S. N.<\/small> and <small>MUKHOPADHYAY, S.<\/small> (2012). A penalized empirical likelihood method in high dimensions. <i>Ann. Statist.<\/i> <b>40<\/b> 2511\u20132540.","DOI":"10.1214\/12-AOS1040"},{"key":"27","unstructured":"<small>LAN, H.<\/small>, <small>CHEN, M.<\/small>, <small>FLOWERS, J. B.<\/small>, <small>YANDELL, B. S.<\/small>, <small>STAPLETON, D. S.<\/small>, <small>MATA, C. M.<\/small>, <small>MUI, E. T.-K.<\/small>, <small>FLOWERS, M. T.<\/small>, <small>SCHUELER, K. L.<\/small> et al. (2006). Combined expression trait correlations and expression quantitative trait locus mapping. <i>PLoS Genet.<\/i> <b>2<\/b> e6."},{"key":"28","doi-asserted-by":"publisher","unstructured":"<small>LENG, C.<\/small> and <small>TANG, C. Y.<\/small> (2012). Penalized empirical likelihood and growing dimensional general estimating equations. <i>Biometrika<\/i> <b>99<\/b> 703\u2013716.","DOI":"10.1093\/biomet\/ass014"},{"key":"29","doi-asserted-by":"crossref","unstructured":"<small>LI, G.<\/small> (1995). Nonparametric likelihood ratio estimation of probabilities for truncated data. <i>J. Amer. Statist. Assoc.<\/i> <b>90<\/b> 997\u20131003.","DOI":"10.1080\/01621459.1995.10476601"},{"key":"30","unstructured":"<small>LI, G.<\/small> and <small>WANG, Q.-H.<\/small> (2003). Empirical likelihood regression analysis for right censored data. <i>Statist. Sinica<\/i> <b>13<\/b> 51\u201368."},{"key":"32","doi-asserted-by":"crossref","unstructured":"<small>MEYER, M.<\/small>, <small>R\u00dcBSAMEN, D.<\/small>, <small>SLANY, R.<\/small>, <small>ILLMER, T.<\/small>, <small>STABLA, K.<\/small>, <small>ROTH, P.<\/small>, <small>STIEWE, T.<\/small>, <small>EILERS, M.<\/small> and <small>NEUBAUER, A.<\/small> (2009). Oncogenic RAS enables DNA damage- and p53-dependent differentiation of acute myeloid leukemia cells in response to chemotherapy. <i>PLoS ONE<\/i> <b>4<\/b> e7768.","DOI":"10.1371\/journal.pone.0007768"},{"key":"33","doi-asserted-by":"publisher","unstructured":"<small>MOON, C.<\/small> and <small>BEDOUI, A.<\/small> (2023). Bayesian elastic net based on empirical likelihood. <i>J. Stat. Comput. Simul.<\/i> <b>93<\/b> 1669\u20131693.","DOI":"10.1080\/00949655.2022.2148254"},{"key":"40","doi-asserted-by":"publisher","unstructured":"<small>PAN, X.-R.<\/small> and <small>ZHOU, M.<\/small> (1999). Using one-parameter sub-family of distributions in empirical likelihood ratio with censored data. <i>J. Statist. Plann. Inference<\/i> <b>75<\/b> 379\u2013392.","DOI":"10.1016\/S0378-3758(98)00156-6"},{"key":"42","doi-asserted-by":"publisher","unstructured":"<small>PARMAR, K.<\/small>, <small>KOCHUPURAKKAL, B. S.<\/small>, <small>LAZARO, J. B.<\/small>, <small>WANG, Z. C.<\/small>, <small>PALAKURTHI, S.<\/small>, <small>KIRSCHMEIER, P. T.<\/small>, <small>YANG, C.<\/small>, <small>SAMBEL, L. A.<\/small>, <small>F\u00c4RKKIL\u00c4, A.<\/small> et al. (2019). The CHK1 inhibitor prexasertib exhibits monotherapy activity in high-grade serous ovarian cancer models and sensitizes to PARP inhibition. <i>Clin. Cancer Res.<\/i> <b>25<\/b> 6127\u20136140.","DOI":"10.1158\/1078-0432.Ccr-19-0448"},{"key":"43","doi-asserted-by":"crossref","unstructured":"<small>P\u00c9REZ-RODR\u00cdGUEZ, P.<\/small>, <small>ACOSTA-PECH, R.<\/small>, <small>SERGIO, P.-E.<\/small>, <small>CRUZ, C. V.<\/small>, <small>ESPINOSA JAVIER, S.<\/small> and <small>CROSSA, J.<\/small> (2018). A Bayesian genomic regression model with skew normal random errors. <i>G<\/i>3 (<i>Bethesda<\/i>) <b>8<\/b> 1771\u20131785.","DOI":"10.1534\/g3.117.300406"},{"key":"44","doi-asserted-by":"publisher","unstructured":"<small>QIN, J.<\/small> (1993). Empirical likelihood in biased sample problems. <i>Ann. Statist.<\/i> <b>21<\/b> 1182\u20131196.","DOI":"10.1214\/aos\/1176349257"},{"key":"45","doi-asserted-by":"publisher","unstructured":"<small>QIN, J.<\/small> (1999). Empirical likelihood ratio based confidence intervals for mixture proportions. <i>Ann. Statist.<\/i> <b>27<\/b> 1368\u20131384.","DOI":"10.1214\/aos\/1017938930"},{"key":"46","doi-asserted-by":"crossref","unstructured":"<small>RAJESHKUMAR, N. V.<\/small>, <small>DE OLIVEIRA, E.<\/small>, <small>OTTENHOF, N.<\/small>, <small>WATTERS, J.<\/small>, <small>BROOKS, D.<\/small>, <small>DEMUTH, T.<\/small>, <small>SHUMWAY, S. D.<\/small>, <small>MIZUARAI, S.<\/small>, <small>HIRAI, H.<\/small> et al. (2011). MK-1775, a potent Wee1 inhibitor, synergizes with gemcitabine to achieve tumor regressions, selectively in p53-deficient pancreatic cancer xenografts. <i>Clin. Cancer Res.<\/i> <b>17<\/b> 2799\u2013806.","DOI":"10.1158\/1078-0432.CCR-10-2580"},{"key":"47","unstructured":"<small>SARMENTO, L. M.<\/small>, <small>P\u00d3VOA, V.<\/small>, <small>NASCIMENTO, R.<\/small>, <small>REAL, G.<\/small>, <small>ANTUNES, I.<\/small>, <small>MARTINS, L. R.<\/small>, <small>MOITA, C.<\/small>, <small>ALVES, P. M.<\/small>, <small>ABECASIS, M.<\/small> et al. (2015). CHK1 overexpression in T-cell acute lymphoblastic leukemia is essential for proliferation and survival by preventing excessive replication stress. <i>Oncogene<\/i> <b>34<\/b> 2978\u201390."},{"key":"50","doi-asserted-by":"crossref","unstructured":"<small>SMITH, H. L.<\/small>, <small>SOUTHGATE, H.<\/small>, <small>TWEDDLE, D. A.<\/small> and <small>CURTIN, N. J.<\/small> (2020). DNA damage checkpoint kinases in cancer. <i>Expert Rev. Mol. Med.<\/i> <b>22<\/b> e2.","DOI":"10.1017\/erm.2020.3"},{"key":"51","doi-asserted-by":"publisher","unstructured":"<small>TANG, C. Y.<\/small> and <small>LENG, C.<\/small> (2010). Penalized high-dimensional empirical likelihood. <i>Biometrika<\/i> <b>97<\/b> 905\u2013920. With supplementary material available online.","DOI":"10.1093\/biomet\/asq057"},{"key":"53","doi-asserted-by":"publisher","unstructured":"<small>TSAO, M.<\/small> (2004). Bounds on coverage probabilities of the empirical likelihood ratio confidence regions. <i>Ann. Statist.<\/i> <b>32<\/b> 1215\u20131221.","DOI":"10.1214\/009053604000000337"},{"key":"54","unstructured":"<small>TSHERNIAK, A.<\/small>, <small>VAZQUEZ, F.<\/small>, <small>MONTGOMERY, P. G.<\/small>, <small>WEIR, B. A.<\/small>, <small>KRYUKOV, G.<\/small>, <small>COWLEY, G. S.<\/small>, <small>GILL, S.<\/small>, <small>HARRINGTON, W. F.<\/small>, <small>PANTEL, S.<\/small> et al. (2017). Defining a cancer dependency map. <i>Cell<\/i> <b>170<\/b> 564\u2013576.e16."},{"key":"55","doi-asserted-by":"publisher","unstructured":"<small>UNNI, A. M.<\/small>, <small>HARBOURNE, B.<\/small>, <small>OH, M. H.<\/small>, <small>WILD, S.<\/small>, <small>FERRARONE, J. R.<\/small>, <small>LOCKWOOD, W. W.<\/small> and <small>VARMUS, H.<\/small> (2018). Hyperactivation of ERK by multiple mechanisms is toxic to RTK-RAS mutation-driven lung adenocarcinoma cells. <i>eLife<\/i> <b>7<\/b>.","DOI":"10.7554\/eLife.33718"},{"key":"56","unstructured":"<small>VANDAELE, W.<\/small> et al. (1976). <i>Participation in Illegitimate Activities<\/i>: <i>I. Ehrlich Revisited<\/i>. Division of Research, Graduate School of Business Administration, Harvard."},{"key":"57","unstructured":"<small>WANG, X.<\/small> and <small>GEORGE, E. I.<\/small> (2007). Adaptive Bayesian criteria in variable selection for generalized linear models. <i>Statist. Sinica<\/i> <b>17<\/b> 667\u2013690."},{"key":"58","doi-asserted-by":"crossref","unstructured":"<small>WARNER, D. F.<\/small> (2016). Defining a diagnostic gene signature for tuberculosis. <i>Lancet Respir. Med.<\/i> <b>4<\/b> 170\u2013171.","DOI":"10.1016\/S2213-2600(16)00063-1"},{"key":"59","doi-asserted-by":"crossref","unstructured":"<small>WEE, P.<\/small> and <small>WANG, Z.<\/small> (2017). Epidermal growth factor receptor cell proliferation signaling pathways. <i>Cancers<\/i> <b>9<\/b> 52.","DOI":"10.3390\/cancers9050052"},{"key":"60","doi-asserted-by":"crossref","unstructured":"<small>XU, C.<\/small>, <small>LU, Z.<\/small>, <small>CHENG, Y.<\/small>, <small>SONG, Q.<\/small>, <small>ZHAO, Y.<\/small> and <small>WANG, X.<\/small> (2026). Supplement to \u201cBayesian empirical likelihood for ultraHigh-dimensional variable selection with application to drug sensitivity.\u201d <a href=\"https:\/\/doi.org\/10.1214\/26-AOAS2210SUPPA\">https:\/\/doi.org\/10.1214\/26-AOAS2210SUPPA<\/a>, <a href=\"https:\/\/doi.org\/10.1214\/26-AOAS2210SUPPB\">https:\/\/doi.org\/10.1214\/26-AOAS2210SUPPB<\/a>","DOI":"10.1214\/26-AOAS2210SUPPB"},{"key":"61","doi-asserted-by":"publisher","unstructured":"<small>ZHANG, C.<\/small>, <small>PENG, K.<\/small>, <small>LIU, Q.<\/small>, <small>HUANG, Q.<\/small> and <small>LIU, T.<\/small> (2024). Adavosertib and beyond: Biomarkers, drug combination and toxicity of WEE1 inhibitors. <i>Crit. Rev. Oncol.\/Hematol.<\/i> <b>193<\/b> 104233.","DOI":"10.1016\/j.critrevonc.2023.104233"},{"key":"63","unstructured":"<small>ZHANG, J.<\/small>, <small>DAI, Q.<\/small>, <small>PARK, D.<\/small> and <small>DENG, X.<\/small> (2016). Targeting DNA replication stress for cancer therapy. <i>Genes<\/i> <b>7<\/b> 51."},{"key":"64","doi-asserted-by":"publisher","unstructured":"<small>ZHAO, T.<\/small>, <small>LIU, H.<\/small>, <small>ZHANG, T.<\/small> et al. (2018). Pathwise coordinate optimization for sparse learning: Algorithm and theory. <i>Ann. Statist.<\/i> <b>46<\/b> 180\u2013218.","DOI":"10.1214\/17-AOS1547"}],"container-title":["The Annals of Applied Statistics"],"original-title":[],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T21:09:10Z","timestamp":1791320950000},"score":1,"resource":{"primary":{"URL":"https:\/\/projecteuclid.org\/journals\/annals-of-applied-statistics\/volume-20\/issue-3\/Bayesian-empirical-likelihood-for-UltraHigh-dimensional-variable-selection-with-application\/10.1214\/26-AOAS2210.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":65,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1214\/26-aoas2210","relation":{},"ISSN":["1932-6157","1941-7330"],"issn-type":[{"value":"1932-6157","type":"print"},{"value":"1941-7330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]}}}