Grants & Publications

  • Aselisewine, W. and Pal, S. (2023), “On the integration of decision trees with mixture cure model”. Statistics in Medicine, 42(23), 4111–4127.
  • Baguley, J.G., Rostami, M.A., Baldrighi, E., Bang, H.W., Dyer, L.A., & Montagna, P.A. (2024). Harpacticoid copepods expand the scope and provide family-level indicators of the Deepwater Horizon oil spill deep-sea impacts. Marine Pollution Bulletin, 202(116343).
  • Barth, J., Yang, Y., Xiao, G., and Wang, X.* (2024), “MetaNorm: Incorporating Meta-analytic Priors into Normalization of NanoString nCounter Data”. Bioinformatics. 40(1), btae024. DOI: 10.1093/bioinformatics/btae024.
  • C. Anand, P. D. Maia, J. Torok, C. Mesias, and A. Raj, The effects of microglia on tauopathy progression can be quantified using Nexopathy in silico (Nexis) models, Scientific Reports (2022), 12: 21170, pp. 1-14.
  • Cheng, Y., Xia, Y., and Wang, X.* (2023), “Bayesian Multi-task Learning for Medicine Recommendation Based on Online Reviews”. Bioinformatics. 39(8), btad491. DOI: 10.1093/bioinformatics/btad491.
  • C. Silva, P. D. Maia, L. M. Stolerman, V. Rolla, L. Velho, Predicting dengue outbreaks in Brazil with manifold learning on climate data, Expert Systems with Applications (2022), 192, pp.1-13.
  • Dasilva, A., Saulo, H., Vila, R., Fiorucci, J. A., and Pal, S. (2023), “Parametric quantile autoregressive moving average models with exogenous terms”. Statistical Papers. DOI:10.1007/s00362-023-01459-4 (to appear).
  • Dulko-Smith, B., Ojeda-May, P., Ådén, J., Wolf-Watz, M., and Nam, K.* (2023), “Mechanistic basis for a connection between the catalytic step and slow opening dynamics of adenylate kinase”. J. Chem. Inf. Model., 63, 1556-1569.
  • Farleigh, K., A. Ascanio, M.E. Farleigh, D.R. Schield, D.C. Card, M. Leal, T.A. Castoe, T. Jezkova, J.A. Rodriguez-Robles (2023), “Signals of differential introgression in the genome of natural hybrids of Caribbean anoles” Molecular Ecology, 2023,6000-60017
  • J. Torok, C. Mesias, P. D. Maia, and A. Raj, Matrix Inversion and Subset Selection (MISS): A novel pipeline for quantitative mapping of diverse cell types across the murine brain, PNAS (2022), 119 (14) e2111786119.
  • Hamm, K.*, Henscheid, N., and Kang, S. (2023), “Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning”. SIAM Journal on Mathematics of Data Science, 5(2), 475-501.
  • Hamm, K* (2023), “Generalized Pseudoskeleton Decompositions”. Linear Algebra and its Applications, 664, 236-252.
  • Hoang, L. Q., Pal, S., Liu, Z., Senkowsky, J., and Tang, L. (2023), “A time-dependent survival analysis for early prognosis of chronic wounds by monitoring wound alkalinity”. International Wound Journal, 20(5), 1459–1475.
  • Kim, S, Chen, W., Sun Mitchell, S. (2023) “Temporal Relationships in Dementia Family Dyadic Communication: Sequential Analysis.” Western Institute of Nursing Conference Proceeding. (Accepted)
  • Liang, X, Guo, Z.C., Wang, L., Li, R.C., Lin, W.W. (2023). “Nearly Optimal Stochastic Approximation for online Principal Subspace Estimation”, 66, 1087-1122.
  • Nam, K.*, Shao, Y., Major, D. T., and Wolf-Watz, M. (2023) “Perspectives on computational enzyme modeling: From mechanisms to design and drug development”. ACS Omega, under revision.
  • Nam, K.*, Arattu Thodika, A. R., Grundström, C., Sauer, U. H., and Wolf-Watz, M. (2023), “Elucidating dynamics of adenylate kinase from enzyme opening to ligand release”. J. Chem. Inf. Model. Accepted.
  • Nam, K.*, Tao, Y., and Ovchinnikov, V. (2023), “.Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway”. J. Phys. Chem. B, 127, 5789-5798.
  • Nam, K.* and Wolf-Watz, M.* (2023), “Protein dynamics; The future is bright and complicated”. Struct. Dyn., 10, 014301.
  • Nie, J.W.*, Wang L., and Zheng, Z.Q. (2023). "Low rank tensor decompositions and approximations." Journal of the Operations Research Society of China, 1-27.
  • Pal, S. and Aselisewine, W. (2023), “A semiparametric promotion time cure model with support vector machine”. Annals of Applied Statistics, 17(3), 2680–2699.
  • Pal, S., Peng, Y., Aselisewine, W., and Barui, S. (2023), “A support vector machine-based cure rate model for interval censored data”. Statistical Methods in Medical Research, 32(12), 2405-2422.
  • Pal, S. and Roy, S. (2023), “On the parameter estimation of Box-Cox transformation cure model”. Statistics in Medicine, 42(15), 2600–2618.
  • Pal, S., Peng, Y., and Aselisewine, W. (2023), “A new approach to modeling the cure rate in the presence of interval censored data”. Computational Statistics. DOI:10.1007/s00180-023-01389-7 (to appear).
  • Pal, S. (2023), “A new cure model with discrete and multiple exposures”. Communications in Statistics-Simulation and Computation (accepted).
  • Pal, S. and Aselisewine, W. (2023), “Machine learning-based cure model in engineering reliability”. In: Developments in Reliability Engineering, Chapter 19 (Eds., M. Ram). Elsevier (accepted).
  • Pal, S. (2023), “Cure rate models”. In International Encyclopedia of Statistical Science Second Edition (Eds., M. Lovric), Springer Berlin, Heidelberg (accepted).
  • Pan, X., Van, R., Pu, J.*, Nam, K.*, Mao, Y.*, and Shao, Y.* (2023), “Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions”. J. Chem. Theory Comput., 19, 8234-8244.
  • Park, S., Kim, J., Wang, X., and Lim, J. (2024), “Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search”. Computational Statistics and Data Analysis. 196. DOI: 10.1016/j.csda.2024.107954.
  • Robben, M., Ramesh, B., Pau, S., Meletis, D., Luber, J. and Demuth, J.P., 2023. scRNA-seq reveals novel genetic pathways and sex chromosome regulation in Tribolium spermatogenesis. bioRxiv, pp.2023-07.
  • Roy, S. and Pal, S. (2023), “Optimal personalized therapies in colon cancer induced immune response using a Fokker-Planck framework”. In: Mathematics and Computer Science Volume 2, Chapter 3 (Eds., S. Ghosh, M. Niranjanamurthy, K. Deyasi, B. Basu Mallik and S. Das), pp.33-47. Scrivener-Wiley.
  • Rostami, M.A., Balmaki, B., Dyer, L.A., Allen, J.M., Sallam, M.F., & Frontalini, F. (2023). Efficient pollen grain classification using pre-trained Convolutional Neural Networks: A comprehensive study. Journal of Big Data, 10, 151.
  • Rostami, M.A., Frontalini, F., Armynot du Châtelet, E., Francescangeli, F., Alves Martins, M.V., De Marco, R., Dinelli, E., Tramontana, M., Dyer, L.A., Abraham, R., Bout-Roumazeilles, V., Delattre, M., & Spagnoli, F. (2023). Understanding the distributions of benthic Foraminifera in the Adriatic Sea with gradient forest and structural equation models. Applied Sciences, 13(2), 794.
  • Shen Y, Kioumourtzoglou MA, Wu H, Spiro A, Vokonas P, Navas-Acien A, Baccarelli AA, Gao F*. (2023). Cohort Network: a knowledge graph towards data dissemination and knowledge-driven discovery for cohort studies. Environmental Science & Technology 57, 8236-8244. Featured as supplemental cover paper.
  • Smith, C.F., C.M. Modahl, D. Ceja-Galindo, K.Y. Larson, S.P. Maroney, L. Bahrabadi, N.P. Brandehoff, B.W. Perry, M.C. MaCabe, D. Petras, B. Lamonte, J.J. Calvete, T.A. Castoe, S.P. Mackessy, K.C. Hansen, A.J. Saviola (2023), “Assessing target specificity of the small molecule inhibitor Marimastat to snake venom toxins: a novel application of thermal proteome profiling”. bioRxiv, 2023.10. 25.564059
  • Smith, C., Z.L. Nikolakis, B.W. Perry, D.R. Schield, N. Balchan, J. Parker, K.C. Hansen, A.J. Saviola, T.A. Castoe and S.P. Mackessy (2023), “Snakes on a plain: complex biotic and abiotic factors determine venom variation in North America’s widest-ranging rattlesnake”. BMC Biology 21,136
  • Smith, C., Z.L. Nikolakis, B.W. Perry, D.R. Schield, J.M. Meik, A.J. Saviola, T.A. Castoe, J. Parker, and S.P. Mackessy, (2023). “The best of both worlds: rattlesnake hybrid zones generate complex combinations of divergent venom phenotypes that retain high toxicity”. Biochimie 213,176-189
  • Smith, C, N.P. Brandehoff, L. Pepin, M.C. McCabe, T.A. Castoe, S.P. Mackessy, T. Nemkov, K.C. Hansen, A.J. Saviola, (2023). “Feasibility of detecting snake envenomation biomarkers from dried blood spots”. Analytical Science Advances, 4,26-36
  • S. Pandya, P. D. Maia, B. Freeze, R.A.L. Menke, K. Tallbot, M.R. Turner, and A. Raj, Modelling seeding and neuroanatomical spread of pathology in amyotrophic lateral sclerosis, NeuroImage (2022), 251, pp. 1-12.
  • Treszoks, J. and Pal, S. (2023), “On the estimation of interval censored destructive negative binomial cure model”. Statistics in Medicine, 42(28), 5113-5134.
  • Wang, K., Yang, Y., Wu, F., Song, B., Wang, X.*, Wang, T.* (2023), “Comparative Analysis of Dimension Reduction Methods for Cytometry by Time-of-Flight Data”. Nature Communications. 14, 1836.DOI: 10.1038/s41467-023-37478-w.
  • Wang, G., Cheng, Y., Xia, Y., Ling, Q., and Wang, X.* (2023), “A Bayesian Semi-supervised Approach to Key Phrase Extraction with Only Positive and Unlabeled Data”. INFORMS Journal on Computing. 5(3), 675-691. DOI: 10.1287/ijoc.2023.1283.
  • Wang, L., Zhang , L.H. , and Li, R.C.* (2023). "Trace ratio optimization with an application to multi-view learning." Mathematical Programming 201(1), 97-131.
  • Wang, L.*, Li, R.C., Lin, W.W. (2023). “ Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions”. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 34(8), 4371 – 4385.
  • Westfall, A.K., S.S. Gopalan, B.W. Perry, R.H. Adams, A.J. Saviola, S.P. Mackessy, and T.A. Castoe, (2023), “Single-cell heterogeneity in snake venom expression is hardwired by co-option of regulators from progressively activated pathways”. Genome Biology and Evolution, 15,evad109
  • Xiong, D., Park, S., Lim, J., Wang, T., and Wang, X.* (2024), “Bayesian Multiple Instance Classification Based on Hierarchical Probit Regression”. The Annals of Applied Statistics.18(1), 80-99. DOI: 10.1214/23-AOAS17.
  • Yang, Y., Wang, K., Lu, Z., Wang, T.*, Wang, X.*, “Cytomulate: Accurate and Efficient Simulation of CyTOF data”. Genome Biology. 24, 262. DOI: 10.1186/s13059-023-03099-1.
  • Zhang, M., Barth, J., Lim, J., and Wang, X.* (2023), “Bayesian Estimation and Testing in Random-Effects Meta-analysis of Rare Binary Events Allowing for Flexible Group Variability”. Statistics in Medicine. 42(11), 1699-1721. DOI:10.1002/sim.9695.
  • Zhang, Y., Zhang, C., Hua, W., Wang, X., Zhang, M., Palmer, K., and Chen, M. (2023), “An Expectation–Maximization Algorithm for Estimating Proportions of Deletions among Bacterial Populations with Application to Study Antibiotic Resistance Gene Transfer in Enterococcus Faecalis”. Marine Life Science & Technology, 5:28–43. DOI: 10.1007/s42995-022-00144-z.
  • Zhang, M., Xiao, O.Y., Lim, J. and Wang, X.*(2023), “Goodness-of-fit Testing for Meta-Analysis of Rare Binary Events”. Scientific Reports. 13, 17712. DOI: 10.1038/s41598-023-44638-x.
  • Zhang, L., Wang, L., Liu, T.M., and Zhu, D.J.* (2023), "Disease2Vec: Representing Alzheimer’s Disease Progression via Disease Embedding Tree", Pharmacological Research, to appear.