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Marina Vannucci
Noah Harding Professor and Chair of Statistics, Rice University

[1996-2000] [2001-2004] [2005-2006] [2007-2008] [2009-2010] [2011-2012] [2013-2014] [2015-2016] [2017-]

Recent papers

[118] Warnick, R., Guindani, M., Erhardt, E., Allen, E., Calhoun, V. and Vannucci, M. (2016). A Bayesian Approach for Estimating Dynamic Functional Connectivity Networks in fMRI Data. JASA, revised.

[117] Mo, Q., Shen, R., Guo, C., Vannucci, M., Chan, K. and Hilsenbeck, S.G. (2016). A Fully Bayesian Latent Variable Model for Integrative Clustering Analysis of Multi-type Omics Data. Biostatistics, minor revision.

[116] Chiang, S., Guindani, M., Yeh, H.J., Dewarz, S., Haneef, Z., Stern, J.M. and Vannucci, M. (2016). A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome After Anterior Temporal Lobe Resection. AOAS, revised.

[115] Li, Q., Guindani, M., Reich, B.J., Bondell, H.D. and Vannucci, M. (2016). A Bayesian Mixture Model for Clustering and Selection of Feature Occurrence Rates under Mean Constraints. Statistical Analysis and Data Mining, revised.

[114] Chapple, A.G., Vannucci, M., Thall, P. and Lim, S.H. (2016). Bayesian variable selection for a semi-competing risks model with multiple components. Computational Statistics and Data Analysis, revised.

2017

[113] Shaddox, E., Stingo, F., Peterson, C.B., Jacobson, S., Cruickshank-Quinn, C., Kechris, K., Bowler, R. and Vannucci, M. (2017). A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD. Statistics in Biosciences, in press.

[112] Chiang, S., Guindani, M., Yeh, H.J., Haneef, Z., Stern, J.M. and Vannucci, M. (2017). A Bayesian Vector Autoregressive Model for Multi-Subject Effective Connectivity Inference using Multi-Modal Neuroimaging Data. Human Brain Mapping, 38, 1311-1332.

[111] Wadsworth, D., Argiento, R., Guindani, M., Galloway-Pena, J., Shelbourne, S.A. and Vannucci, M. (2016). An Integrative Bayesian Dirichlet-Multinomial Regression Model for the Analysis of Taxonomic Abundances in Microbiome Data. BMC Bioinformatics 18:94, DOI 10.1186/s12859-017-1516-0.

2015-2016

[110] Zhang, L., Guindani, M., Versace, F., Engelmann, J.M. and Vannucci, M. (2016). A Spatio-Temporal Nonparametric Bayesian Model of Multi-Subject fMRI Data. Annals of Applied Statistics, 10(2), 638-666. See also the supplementary material.

[109] Peterson, C.B., Stingo, F.C. and Vannucci, M. (2016). Joint Bayesian variable and graph selection for regression models with network-structured predictors. Statistics in Medicine, 35(7), 1017-1031.

[108] Villagran, A., Huerta, G., Vannucci, M., Jackson, C.S. and Nosedal, A. (2016). Non-Parametric Sampling Approximation via Voronoi Tessellations. Communications in Statistics - Simulation & Computation, 45, 1-20.

[107] Trevino, V., Cassese, A., Nagy, Z., Zhuang, X., Herbert, J., Antzack, P., Clarke, K., Davies, N., Rahman, A., Campbell, M., Guindani, M., Bicknell, R., Vannucci, M. and Falciani, F. (2016). A Network Biology Approach Identifies Molecular Cross-talk between Normal Prostate Epithelial and Prostate Carcinoma Cells. PLoS Computational Biology, 12(4), e1004884.

[106] Li, Q., Dahl, D.B., Vannucci, M., Joo, H. and Tsai, J.W. (2016). KScons: A Bayesian Approach for Protein Residue Contact Prediction using the Knob-Socket Model of Protein Tertiary Structure. Bioinformatics, 32(24), 3774-3781.

[105] Chiang, S., Cassese, A., Guindani, M., Vannucci, M., Yeh, H.J., Haneef, Z. and Stern, J.M. (2016). Time-dependence of Graph Theory Metrics in Functional Connectivity Analysis. NeuroImage, 125, 601-615.

[104] Teo, I., Fronczyk, K., Guindani, M., Vannucci, M., Ulfers, S., Hanasono, M. and Fingeret, M.C. (2016). Salient Body Image and Psychosocial Concerns of Cancer Patients Undergoing Head and Neck Reconstruction. Head and Neck, 38(7), 1035-1042.

[103] Cassese, A., Guindani, M. and Vannucci, M. (2016). iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH data. In Statistical Analysis for High-Dimensional Data - The Abel Symposium 2014, Frigessi, A., Buhlmann, P., Glad, I., Langaas, M., Richardson, S. and Vannucci, M. (Eds). Springer Verlag, 105-123.

[102] Peterson, C.B., Stingo, F.C. and Vannucci, M. (2015). Bayesian Inference of Multiple Gaussian Graphical Models. Journal of the American Statistical Association, 110, 159-174.

[101] Cassese, A., Guindani, M., Antczak, P., Falciani, F. and Vannucci, M. (2015). A Bayesian Model for the Identification of Differentially Expressed Genes in Daphnia Magna Exposed to Munition Pollutants. Biometrics, 71, 803-811.

[100] Waters, A.E., Fronczyk, K., Guindani, M., Baraniuk, R.G. and Vannucci, M. (2015). A Bayesian Nonparametric Approach for the Analysis of Multiple Categorical Item Responses. Journal of Statistical Planning and Inference, 166, 52-66.

[99] Stingo, F.C., Swartz, M.D. and Vannucci, M. (2015). A Bayesian Approach for the Identification of Genes and Gene-level SNP Aggregates in a Genetic Analysis of Cancer Data. Statistics and Its Interface, 8(2), 137-151.

[98] Zhang, L., Guindani, M. and Vannucci, M. (2015). Bayesian models for functional magnetic resonance imaging data analysis. WIREs Computational Statistics, 7, 21-41 (invited contribution).

[97] Fronczyk, K., Guindani, M. Hobbs, B.P., Ng, C. and Vannucci, M. (2015). A Bayesian nonparametric approach for functional data classification with application to hepatic tissue characterization. Cancer Informatics, 14(S5), 151-162.

[96] Rembach, A., Stingo, F., Peterson, C., Vannucci, M., Do, K-A., Wilson, W.J., Macaulay, S.L., Ryan, T.M., Martins, R.N., Ames, D., Masters, C.L., Doecke, J.D. and the AIBL Research Group (2015). Bayesian graphical network analyses reveal complex biological interactions specific to Alzheimer's Disease. Journal of Alzheimer's Disease, 44(3), 917-925.

2013-2014

[95] Cassese, A., Guindani, M., Tadesse, M., Falciani, F. and Vannucci, M. (2014). A Hierarchical Bayesian Model for Inference on Copy Number Variants and their Association to Gene Expression. Annals of Applied Statistics, 8(1), 148-175.

[94] Zhang, L., Guindani, M., Versace, F. and Vannucci, M. (2014). A Spatio-Temporal Nonparametric Bayesian Variable Selection Model of fMRI Data for Clustering Correlated Time Courses. NeuroImage, 95, 162-175. See also the supplementary material.

[93] Li, Q., Dahl, D.B., Vannucci, M., Joo, H. and Tsai, J.W. (2014). Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction. PLoS ONE, 9(10), e109832.

[92] Cassese, A., Guindani, A. and Vannucci, M. (2014). A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection. Cancer Informatics, 13(S2) 29-37. See also the supplementary material.

[91] Cowley, A.W., Moreno, C.P., Jacob, H., Peterson, C.B., Stingo, F.C.,Ahn, K.W., Liu, P., Vannucci, M., Laud, P.W., Reddy, P., Lazar, J., Evans, L., Yang, C., Kurth, T. and Liang, M. (2014). Characterization of Biological Pathways Mediating a 1.37mbp Genomic Region Protective of Hypertension in Dahl S. Rats. Physiological Genomics, 46, 398-410.

[90] Fronczyk. K., Guindani, M., Vannucci, M., Palange, A. and Decuzzi, P. (2014). A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles. Computational Mechanics, 53(3), 539-547.

[89] Shetty, A.N., Chiang, S., Maletic-Savatic, M., Kasprian, G., Vannucci, M. and Lee, W. (2014). Spatial Mapping of Translational Diffusion Coefficients Using Diffusion-Tensor Imaging: A Mathematical Description. Concepts in Magnetic Resonance Part A, 43(1), 1-27.

[88] Stingo, F.C., Guindani, M., Vannucci, M. and Calhoun, V. (2013). An Integrative Bayesian Modeling Approach to Imaging Genetics. Journal of the American Statistical Association, 108, 876-891.

[87] Jeong, J., Vannucci, M. and Ko, K. (2013). A Wavelet-based Bayesian Approach to Regression Models with Long Memory Errors and its Application to fMRI Data. Biometrics, 69(1), 184-196.

[86] Allen, G.I., Peterson, C.B., Vannucci, M. and Maletic-Savatic, M. (2013). Regularized Partial Least Squares with an Application to NMR Spectroscopy. Statistical Analysis and Data Mining, 6(4), 302-314.

[85] Peterson, C., Vannucci, M., Karakas, C., Choi, W., Ma, L. and Maletic-Savatic, M. (2013). Inferring Metabolic Networks Using the Bayesian Adaptive Graphical Lasso with Informative Priors. Statistics and Its Interface, 6, 547-558.

[84] Brownlees, C. and Vannucci, M. (2013). A Bayesian approach for capturing daily heterogeneity in intradaily durations time series. Studies in Nonlinear Dynamics and Econometrics, 17(1), 21-46.

[83] Swartz, M.D., Peterson, C.B., Lupo, P.J., Wu, X., Forman, M.R., Spitz, M.R., Hernandez, L.M., Vannucci, M. and Shete, S. (2013). Investigating multiple candidate genes and nutrients in the folate metabolism pathway to detect genetic and nutritional risk factors for lung cancer. PLoS ONE, 8(1), e53475.

[82] Day, R., Joo, H., Chavan, A., Lennox, K.P., Chen, Y.A., Dahl, D.B., Vannucci, M. and Tsai, J.W. (2013). Understanding the General Packing Rearrangements Required for Successful Template Based Modeling of Protein Structure from a CASP Experiment. Computational Biology and Chemistry, 42, 40-48.

[81] Yang, C., Stingo, F.C., Ahn, K.W., Liu, P., Vannucci, M., Laud, P.W., Skelton, M., O'Connor, P., Kurth, T., Moreno, C., Tsaih, S.W., Patone, G., Humme, O., Jacob, H.J., Liang, M. and Cowley, A.W. (2013). Increased Proliferative Cells in the Medullary Thick Ascending Limb of the Loop of Henle in the Dahl Salt-Sensitive Rat. Hypertension, 61(1), 208-215.

[80] Peterson, C.B., Swartz, M.D., Shete, S. and Vannucci, M. (2013). Bayesian Model Averaging for Genetic Association Studies. In Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data, Kim-Anh Do, Zhaohui Steve Qin and Marina Vannucci (Eds). Cambridge University Press, 208-223.

[79] Stingo, F.C. and Vannucci, M. (2013). Bayesian Models for Integrative Genomics. In Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data, Kim-Anh Do, Zhaohui Steve Qin and Marina Vannucci (Eds). Cambridge University Press, 272-291.

2011-2012

[78] Stingo, F.C., Vannucci, M. and Downey, G. (2012). Bayesian Wavelet-based Curve Classification via Discriminant Analysis with Markov Random Tree Priors. Statistica Sinica, 22, 465-488.

[77] Lee, S.H., Lim, J., Li, E., Vannucci, M. and Petkova, E. (2012). Order test for high-dimensional two sample means. Journal of Statistical Planning and Inference, 142, 2719-2725.

[76] Flores, R.J., Li, Y., Yu, A., Shen, J., Lau, S.S., Rao, P.H., Vannucci, M., Lau, C.C. and Man, T.K. (2012). A Systems Biology Approach Reveals Common Metastatic Pathway in Osteosarcoma. BMC Systems Biology, 6:50.

[75] Stingo, F.C., Chen Y.A., Tadesse, M.G. and Vannucci, M. (2011). Incorporating Biological Information into Linear Models: A Bayesian Approach to the Selection of Pathways and Genes. Annals of Applied Statistics, 5(3), 1978-2002. Supplementary Material.

[74] Savitsky, T., Vannucci, M. and Sha, N. (2011). Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies. Statistical Science, 26(1), 130-149.

[73] Kwon, D.W., Landi, M.T., Vannucci, M., Issaq, H.J., Prieto, D. and Pfeiffer, R.M. (2011). An efficient stochastic search for Bayesian variable selection with high-dimensional correlated predictors. Computational Statistics and Data Analysis, 55(10), 2807-2818.

[72] Chavan, A.G., Joo, H., Day, R., Lennox, K.P., Sukhanov, P., Dahl, D.B., Vannucci, M. and Tsai, J.W. (2011). Near-Native Protein Loop Modeling using Nonparametric Density Estimation Accommodating Sparcity. PLoS Computational Biology,7(10), e10002234.

[71] Stingo, F.C. and Vannucci, M. (2011). Variable Selection for Discriminant Analysis with Markov Random Field Priors for the Analysis of Microarray Data. Bioinformatics, 27(4), 495-501.

[70] Trevino, V., Tadesse, M.G., Vannucci, M., Al-Shahrour, F., Antczak, P., Durant, S., Bikfalvi, A., Dopazo, J., Campbell, M.J. and Falciani, F. (2011). Analysis of normal-tumour tissue interaction in tumours: Prediction of prostate cancer features from the molecular profile of adjacent normal cells. PLoS ONE, 6(3), e16492.

[69] Preter, M., Lee, S.H., Petkova. E., Vannucci, M., Kim, S. and Klein, D.F. (2011). Controlled cross-over study in normal subjects of naloxone-preceding-lactate infusions; respiratory and subjective responses: relationship to endogenous opioid system, suffocation false alarm theory and childhood parental loss. Psychological Medicine, 41(2), 385-394.

[68] Cho, Y., Kim, H., Turner, N.D., Mann, J.C., Wei, J., Taddeo, S.S., Davidson, L.A., Wang, N., Vannucci, M., Carroll, R.J., Chapkin, R.S. and Lupton, J.R. (2011). A chemoprotective fish oil/pectin diet temporally alters gene expression profiles in exfoliated rat colonocytes throughout oncogenesis. Journal of Nutrition, 141(6), 1029-35.

[67] Vannucci, M. and Stingo, F.C. (2011). Bayesian Models for Variable Selection that Incorporate Biological Information (with discussion). In Bayesian Statistics 9 (J.M. Bernardo, M.J. Bayarri, J.O. Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith and M. West eds.). Oxford: University Press, 659-678.

2009-2010

[66] Stingo, F.C., Chen, Y.A., Vannucci, M., Barrier, M. and Mirkes, P.E. (2010). A Bayesian graphical modeling approach to microRNA regulatory network inference. Annals of Applied Statistics, 4(4), 2024-2048. Matlab code and data available here.

[65] Lennox, K.P., Dahl, D.B., Vannucci, M., Day, R. and Tsai, J.W. (2010). A Dirichlet process mixture of hidden Markov models for protein structure prediction. Annals of Applied Statistics, 4(2), 916-942. click here for the software used in this paper.

[64] Zhu, H., Vannucci, M. and Cox, D.D. (2010). A Bayesian hierarchical model for classification with selection of functional predictors. Biometrics, 66(2), 463-473.

[63] Savitsky, T. and Vannucci, M. (2010). Spiked Dirichlet Process Priors for Gaussian Process Models. Journal of Probability and Statistics, 2010, article ID 201489, 14 pages.

[62] Koshelev, M., Lohrenz, T., Vannucci, M. and Montague, P.R. (2010). Biosensor Approach to Psychopathology Classification. PLoS Computational Biology, 6(10), e1000966. Press release.

[61] Day, R., Lennox, K.P., Dahl, D.B., Vannucci, M. and Tsai, J.W. (2010). Characterizing the regularity of tetrahedral packing motifs in protein tertiary structure. Bioinformatics, 26(24), 3059-3066.

[60] Guo, B., Villagran, A., Vannucci, M., Wang, J., Davis, C., Man, T.K., Lau, C. and Guerra, R. (2010). Bayesian Estimation of Genomic Copy Number with Single Nucleotide Polymorphism Genotyping Arrays. BMC Research Notes, 3:350.

[59] Zreik, T.G., Mazloom, A., Chen, Y., Vannucci, M., Fulton, S., Hadziahmetovic, M., Asmar, N., Munkarah, A.R., Ayoub, C.M., Shihadeh, F., Berjawi, G., Hannoun, A., Zalloua, P., Wogan, C. and Dabaja, B. (2010). Fertility Drugs and the Risk of Breast Cancer: A Meta-Analysis and Review. Breast Cancer Research and Treatment, 124(1), 13-26.

[58] Jeong, J., Vannucci, M., Do, K.-A., Broom, B., Kim, S., Sha, N., Tadesse, M., Yan, K. and Pusztai, L. (2010). Gene selection for the identification of biomarkers in high-throughput data. In Bayesian Modeling in Bioinformatics, Dipak K. Dey, Samiran Ghosh and Bani Mallick (Eds). Chapman and Hall/CRC press, 233-254.

[57] Lennox, K., Dahl, D.B., Vannucci, M. and Tsai, J. (2009). Density estimation for protein conformation angles using a von Mises distribution and Bayesian nonparametrics. Journal of the American Statistical Association, 104, 586-596. Correction in 104, 1728.

[56] Kim, S., Dahl, D.B. and Vannucci, M. (2009). Spiked Dirichlet process prior for Bayesian multiple hypothesis testing in random effects models. Bayesian Analysis, 4(4), 707-732.

[55] Ko, K., Qu, L. and Vannucci, M. (2009). Wavelet-based Bayesian estimation of partially linear regression models with long memory errors. Statistica Sinica, 19(4), 1463-1478.

[54] Gardoni, P., Trejo, D., Vannucci, M. and Bhattacharjee, C. (2009). Probability Models for the Modulus of Elasticity of Self Consolidated Concrete: A Bayesian Approach. ASCE Journal of Engineering Mechanics, 135, 295-306.

[53] Small, C.M., Carney, G.E., Mo, Q., Vannucci, M. and Jones, A.G. (2009). A microarray analysis of sex- and gonad-biased gene expression in the zebrafish: Evidence for masculinization of the transcriptome. BMC Genomics, 10:579.

[52] Swanson, R., Vannucci, M. and Tsai, J. (2009). Information theory provides a comprehensive framework for the evaluation of protein structure predictions. Proteins: Structure, Function, and Bioinformatics, 74(3), 701-711.

[51] Jayaraman, A., Maguire, T., Vemula, M., Kwon, D.W., Vannucci, M., Berthiaume, F., and Yarmush, M.L. (2009). Gene expression profiling of long-term changes in rat liver following burn-injury. Journal of Surgical Research, 152(1), 3-17.

[50] Popovic, N., Bridenbaugh, E.A., Neiger, J.D., Hu, J.J., Vannucci, M., Mo, Q., Trzeciakowski, J., Miller, M.W., Fossum, T.W., Humphrey, J.D. and Wilson, E. (2009). Transforming growth factor beta signaling in hypertensive remodeling of porcine aorta. American Journal of Physiology: Heart and Circulatory Physiology, 297, 2044-2053.

2007-2008

[49] Lee, S., Lim, J., Vannucci, M., Petkova, E., Preter, M. and Klein, D.F. (2008). Order-Preserving Dimension Reduction Test for the Dominance of Two Mean Curves with Application to Tidal Volume Curves. Biometrics, 64(3), 931-939.

[48] Ortega, F., Semeith, K., Turan, N., Compton, R., Trevino, V., Vannucci, M. and Falciani, F. (2008). Models and computational strategies linking physiological response to molecular networks from large-scale data. Philosophical Transactions of the Royal Society A, 366, 3067-3089.

[47] Dahl, D.B., Mo, Q. and Vannucci, M. (2008). Simultaneous inference for multiple testing and clustering via a Dirichlet process mixture model. Statistical Modelling: An International Journal, 8(1), 23-39.

[46] Swartz, M.D., Mo, Q., Murphy, M.E., Turner, N., Lupton, J., Hong, M.Y. and Vannucci, M. (2008). Bayesian variable selection in clustering high dimensional data with substructure. Journal of Agricultural, Biological and Environmental Statistics, 13(4), 407-423.

[45] Kwon, D.W., Vannucci, M., Song, J.J., Jeong, J. and Pfeiffer, R. (2008). A novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noise. Proteomics, 8(15), 3019-3029.

[44] Cruz-Marcelo, A., Guerra, R., Vannucci, M., Li, Y., Lau, C. and Man, C. (2008). Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. Bioinformatics, 24(19), 2129-2136.

[43] Dahl, D., Bohannan, Z., Mo, Q., Vannucci, M. and Tsai, J. (2008). Assessing side-chain perturbations of the protein backbone: A knowledge based classification of residue Ramachandran space. Journal of Molecular Biology, 378, 749-758.

[42] Kagiampakis, I., Jin, H., Kim, S, Vannucci, M., LiWang, P.J. and Tsai, J. (2008). Conservation of unfavorable sequence motifs that contribute to chemokine quaternary state. Biochemistry, 47, 10637-10648.

[41] Di Martino, A., Ghaffari, M., Curchack, J., Philip Reiss, P., Hyde, C., Vannucci, M., Petkova, E., Klein, D.F. and Castellanos, F.X. (2008). Decomposing intra-subject variability in children with attention-deficit/hyperactivity disorder. Biological Psychiatry, 64(7), 607-614.

[40] Kwon, D.W., Tadesse, M.G., Sha, N., Pfeiffer, R.M. and Vannucci, M. (2007). Identifying biomarkers from mass spectrometry data with ordinal outcome. Cancer Informatics, 3, 19-28.

[39] Kim, S., Tsai, J., Kagiampakis, I., LiWang, P. and Vannucci, M. (2007). Detecting protein dissimilarities in multiple alignments using Bayesian variable selection. Bioinformatics, 23(2), 245-246.

[38] Alhamad, M.N., Stuth, J. and Vannucci, M. (2007). Biophysical modeling and NDVI time series to project near-term forage supply: Spectral analysis aided by wavelet denoising and ARIMA modeling. International Journal of Remote Sensing, 28(11), 2513-2548.

2005-2006

[37] Kim, S., Tadesse, M.G. and Vannucci, M. (2006). Variable selection in clustering via Dirichlet process mixture models. Biometrika, 93(4), 877-893.

[36] Ko, K. and Vannucci, M. (2006). Bayesian wavelet analysis of autoregressive fractionally integrated moving-average processes. Journal of Statistical Planning and Inference, 136(10), 3415-3434.

[35] Sha, N., Tadesse, M.G. and Vannucci, M. (2006). Bayesian variable selection for the analysis of microarray data with censored outcome. Bioinformatics, 22(18), 2262-2268. Supplementary material. Click here for the Matlab code used in this paper.

[34] Kwon, D.W., Ko, K., Vannucci, M., Reddy, A.L.N. and Kim, S. (2006). Wavelet methods for the detection of anomalies and their application to network traffic analysis. Quality and Reliability Engineering International, 22, 1-17.

[33] Ko, K. and Vannucci, M. (2006). Bayesian wavelet-based methods for the detection of multiple changes of the long memory parameter. IEEE Transactions on Signal Processing, 54(11), 4461-4470.

[32] Tadesse, M.G., Sha, N., Kim, S. and Vannucci, M. (2006). Identification of biomarkers in classification and clustering of high-throughput data. In Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Mueller and Marina Vannucci (Eds). Cambridge University Press, 97-115.

[31] Kwon, D.W., Kim, S., Dahl, D., Swartz, M., Tadesse, M.G. and Vannucci, M. (2006). Identification of DNA regulatory motifs and regulators by integrating gene expression and sequence data. In Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Mueller and Marina Vannucci (Eds). Cambridge University Press, 333-346.

[30] Tadesse, M.G., Sha, N. and Vannucci, M. (2005). Bayesian variable selection in clustering high-dimensional data. Journal of the American Statistical Association, 100, 602-617.

[29] Park, C.G., Vannucci, M. and Hart, J.D. (2005). Bayesian Methods for Wavelet Series in Single-Index Models. Journal of Computational and Graphical Statistics, 14(4), 770-794.

[28] Tadesse, M.G., Ibrahim, J.G., Vannucci, M. and Gentleman, R. (2005). Wavelet thresholding with Bayesian false discovery rate control. Biometrics, 61, 25-35.

[27] Fabbroni, L., Vannucci, M., Cuoco, E., Losurdo, G., Mazzoni, M. and Stanga, R. (2005). Wavelet tests for the detection of transients in the VIRGO interferometric gravitational wave detector. IEEE Transactions on Instrumentation and Measurement, 54(1), 151-162.

[26] Vannucci, M., Sha, N. and Brown, P.J. (2005). NIR and mass spectra classification: Bayesian methods for wavelet-based feature selection. Chemometrics and Intelligent Laboratory Systems, 77, 139-148.

2001-2004

[25] Sha, N., Vannucci, M., Tadesse, M.G., Brown, P.J., Dragoni, I., Davies, N., Roberts, T.C., Contestabile, A., Salmon, N., Buckley, C. and Falciani, F. (2004). Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage. Biometrics, 60(3), 812-819. Click here for the Matlab code used in this paper.

[24] Gabbanini, F., Vannucci, M., Bartoli, G. and Moro, A. (2004). Wavelet Packet Methods for the Analysis of Variance of Time Series with Application to Crack Widths on the Brunelleschi Dome. Journal of Computational and Graphical Statistics, 13(3), 639-658.

[23] Tadesse, M.G., Vannucci, M. and Lio, P. (2004). Identification of DNA regulatory motifs using Bayesian variable selection. Bioinformatics, 20(16), 2553-2561. Click here for the Matlab code used in this paper.

[22] Davies, N., Tadesse, M.G., Vannucci, M., Kikuchi, H., Trevino, V., Sarti, D., Dragoni, I., Contestabile, A., Zanders, E. and Falciani, F. (2004). Making sense of molecular signatures in the immune system. Journal of Combinatorial Chemistry and High Throughput Screening, 7(3), 231-238.

[21] Kim, S.S., Reddy, A.L.N. and Vannucci, M. (2004). Detecting traffic anomalies through aggregate analysis of packet header data. In Proceedings of the 3rd IFIP-TC6 Networking conference. Mitrou, N. et al. (Editors), Lecture Notes in Computer Science, vol. 3042, Springer Verlag, 1047-1059 (refereed volume, 103/539=19.1% acceptance rate).

[20] Kim, S.S., Reddy, A.L.N. and Vannucci, M. (2004). Detecting traffic anomalies using discrete wavelet transform. In Proceedings of the International Conference on Information Networking. Kahng, H.K. and Goto, S. (Editors), Lecture Notes in Computer Science, vol. 3090, Springer Verlag, 951-961 (refereed volume, 104/341=30.5% acceptance rate).

[19] Morris, J.S., Vannucci, M., Brown, P.J. and Carroll, R.J. (2003). Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis (with discussion). Journal of the American Statistical Association, 98, 573-597.

[18] Vannucci, M., Brown, P.J. and Fearn, T. (2003). A decision theoretical approach to wavelet regression on curves with a high number of regressors. Journal of Statistical Planning and Inference, 112(1-2), 195-212.

[17] Sha, N., Vannucci, M., Brown, P.J., Trower, M.K., Amphlett, G. and Falciani, F. (2003). Gene selection in arthritis classification with large-scale microarray expression profiles. Comparative and Functional Genomics, 4(2), 171-181.

[16] Lee, K.E., Sha, N., Dougherty, E., Vannucci, M. and Mallick, B.K. (2003). Gene selection: A Bayesian variable selection approach. Bioinformatics, 19(1), 90-97.

[15] Lio, P. and Vannucci, M. (2003). Investigating the evolution and structure of chemokine receptors. Gene, 317, 29-37.

[14] Brown, P.J., Vannucci, M. and Fearn, T. (2002). Bayes model averaging with selection of regressors. Journal of the Royal Statistical Society, Series B, 64(3), 519-536. Click here for the dataset used in this paper and Matlab code.

[13] Brown, P.J., Fearn, T. and Vannucci, M. (2001). Bayesian wavelet regression on curves with application to a spectroscopic calibration problem. Journal of the American Statistical Association, 96, 398-408. Click here for the dataset used in this paper (also available as part of the R package ppls - Penalized Partial Least Squares) and here for the Matlab code (WavBox toolbox required).

[12] Vannucci, M. and Lio, P. (2001). Non-decimated wavelet analysis of biological sequences: Applications to protein structure and genomics. Sankhya, Series B, 63(2), 218-233.

[11] Vannucci, M., Brown, P.J. and Fearn, T. (2001). Predictor selection for model averaging. In Bayesian methods with applications to science, policy and official statistics. (Eds E.I. George and P. Nanopoulos), Eurostat: Luxemburg, 553-562.

1996-2000

[10] Lio, P. and Vannucci, M. (2000). Wavelet change-point prediction of transmembrane proteins. Bioinformatics, 16(4), 376-382.

[9] Lio, P. and Vannucci, M. (2000). Finding pathogenicity islands and gene transfer events in genome data. Bioinformatics, 16(10), 932-940.

[8] Spiegelman, C., Bennett, J., Vannucci, M., McShane, M.J. and Cote, G. (2000). A transparent tool for seemingly difficult calibrations: The parallel calibration method. Analytical Chemistry, 72(1), 135-140. Correction in 72(8), p. 1944.

[7] Brown, P.J., Fearn, T. and Vannucci, M. (1999). The choice of variables in multivariate regression: a non-conjugate Bayesian decision theory approach. Biometrika, 86(3), 635-648. Click Matlab code.

[6] Vannucci, M. and Corradi, F. (1999). Covariance structure of wavelet coefficients: Theory and models in a Bayesian perspective. Journal of the Royal Statistical Society, Series B, 61(4), 971-986. Click Matlab code (WavBox toolbox required).

[5] Vannucci, M. and Corradi, F. (1999). Modeling dependence in the wavelet domain. In Bayesian Inference in Wavelet based Models. (Eds P. Muller and B. Vidakovic), New York: Springer-Verlag, 173-186.

[4] Brown, P.J., Vannucci, M. and Fearn, T. (1998). Multivariate Bayesian variable selection and prediction. Journal of the Royal Statistical Society, Series B, 60(3), 627-641. Click Matlab code.

[3] Brown, P.J., Vannucci, M. and Fearn, T. (1998). Bayesian wavelength selection in multicomponent analysis. Journal of Chemometrics, 12(3), 173-182. Click Matlab code.

[2] Vannucci, M. and Vidakovic, B. (1997). Preventing the Dirac disaster: Wavelet based density estimation. Journal of the Italian Statistical Society, 6(2), 145-159.

[1] Brown, P.J., Vannucci, M. and Fearn, T. (1997). Multivariate Bayesian wavelength selection for NIR spectra applied to biscuit dough pieces. Proceedings of the 5a Journees Europeennes Agro-Industrie et Methodes Statistique, 19.1-19.11. (refereed volume).