**Books**

U.M. Braga-Neto, *Fundamentals of Pattern Recognition and Machine Learning,* Springer, 2020.

Book Website

U.M. Braga-Neto and E.R. Dougherty, *Error Estimation for Pattern Recognition,* Wiley-IEEE, 2015.

Google Books

**Book Chapters**

U.M. Braga-Neto, E. Arslan, U. Banerjee, and A. Bahadorinejad, “Bayesian Classification of Genomic Big Data,” In *Signal Processing and Machine Learning for Biomedical Big Data*. Edited by E. Sedjic and T. Falk. CRC Press, 2018.

R. Dhalia, L. Gil, E. Nascimento, U.M. Braga-Neto and E.T.A. Marques Jr., “Epitope Mapping: Rational Search for the Development of vaccines Against Chronic Diseases” (in Portuguese). In *Epidemiology, Policy, and Determining Factors of Chronic Diseases in Brazil* (in Portuguese). Edited by Eduardo Freese. Editora Universitária UFPE, Recife, Brazil, 2006, pp. 321-340

U.M. Braga-Neto and E. Dougherty, “Classification.” In *Genomic Signal Processing and Statistics*, Edited by E. Dougherty, I. Shmulevich, J. Chen and Z. J. Wang, EURASIP Book Series on Signal Processing and Communication, Hindawi Publishing Corporation, 2005.

**Ph.D. Dissertation**

U.M. Braga-Neto, *Connectivity in Image Processing and Analysis: Theory, Multiscale Extensions and Applications*. Ph.D. Thesis, Baltimore, 2002. pdf

**Recent Preprints**

L. McClenny and U. Braga-Neto (2020), “Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism,” ArXiv

L. McClenny, M. Haile, V. Attari, B. Sadler, U. Braga-Neto and R. Arroyave (2020), “Deep Multimodal Transfer-Learned Regression in Data-Poor Domains,” ArXiv

C. Davi and U.M. Braga-Neto (2020), “A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes.” ArXiv.

**Peer-Reviewed Publications (Journal and Conference Articles)**

139. L. McClenny and U.M. Braga-Neto, “Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism,” Proceedings of the AAAI Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences (AAAI-MLPS), March 22-24, 2021.

138. Y. Tan, F. Lima Neto and U.M. Braga-Neto, “PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks from Time Series Data,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, In Press, 2020. BioArXiV

137. U.M. Braga-Neto and E.R. Dougherty, “Machine Learning Requires Probability and Statistics,” *IEEE Signal Processing Magazine*, Vol. 37, No. 4, Jul 2020, pp.118-122.

136. A. Bahadorinejad, M. Imani, and U. Braga-Neto, “Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems,” *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, Vol. 17, No. 4, July-August 2020, pp. 1105-1114.

135. Y. Tan, F. Lima Neto and U.M. Braga-Neto, “Inference Of Protein-Protein Interaction Networks From Liquid-Chomatographic Mass-Spectrometry Data By Aproximate Bayesian Computation-Sequential Monte Carlo Sampling,” IEEE International Workshop on Machine Learning for Signal Processing, Sep 2020, Espoo, Finland.

134. C. Kunselman, V. Attari, L. McClenny, U. Braga-Neto and R. Arroyave, “Semi-supervised Learning Approaches to Class Assignment in Ambiguous Microstructures,” *Acta Materialia*, Vol. 188, Apr 2020, pp. 49-62.

133. M. Imani, E.R. Dougherty and U.M. Braga-Neto, “Boolean Kalman Filter and Smoother Under Model Uncertainty,” *Automatica*, Vol. 111, Jan 2020, p. 108609.

132. M. Imani and U.M. Braga-Neto, “Control of Gene Regulatory Networks using Bayesian Inverse Reinforcement Learning,” *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, Special Issue on Advanced Machine Learning Techniques for Bioinformatics, Vol. 16, No. 4, July-August 2019, pp. 1250-1261.

131. S. Xie and U. Braga-Neto, “On the Bias of Precision Estimation Under Separate Sampling”, *Cancer Informatics*, Vol 18, Jul 2019.

130. E. Hajiramezanali, M. Imani, U. Braga-Neto, X. Qian and E.R. Dougherty, “Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty,” *BMC Genomics*, Vol. 20, Article Number 435, 2019.

129. M. Imani, S. F. Ghoreishi, D. Allaire, and U. Braga-Neto, “MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models”, In Proceedings of *AAAI Conference on Artificial Intelligence (AAAI’19) *, Vol. 33, No. 1, pp. 7858-7865, 2019 (16.2% accept rate).

128. C. Davi, A. Pastor, T. Oliveira, F.B. Lima Neto, U. Braga-Neto, A.W. Bigham, M. Bamshad, E.T.A. Marques, B. Acioli-Santos, “Severe Dengue Prognosis Using Human Genome Data and Machine Learning,” *IEEE Transactions on Biomedical Engineering*. Vol 66, No 10, pp. 2861-2868, Oct 2019.

127. M. Imani, S. F. Ghoreishi, and U. Braga-Neto, “Bayesian Decision Making in Uncertain MDPs with Large or Infinite-Dimensional Spaces”, In *Advances in Neural Information Processing Systems 31 (NIPS’2018)*, Dec 2018.

126. C. Davi, A. Pastor, T. Oliveira, F.B. Lima Neto, U. Braga-Neto, A.W. Bigham, M. Bamshad, E.T.A. Marques, B. Acioli-Santos, “Computational Intelligence applied to Human Genome Data for the Dengue Severity Prognosis,” Proceedings of the 11th Brazilian Symposium on Bioinformatics, BSB 2018, Niterói, Brazil, Oct–Nov, 2018. **Best Short Paper Award**

125. Y. Tan, F.B. Lima Neto, U. Braga-Neto, “Inference of Gene Regulatory Networks by Maximum-Likelihood Adaptive Filtering and Discrete Fish School Search,” In *Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP’18)*, September 2018.

124. E. Hajiramezanali, M. Imani, U. Braga-Neto, X. Qian and E.R. Dougherty, “Scalable Optimal Bayesian Classification of Single-Cell Trajectories under Regulatory Model Uncertainty,” Proceedings of the 5th International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2018), Washington, DC, Aug 2018.

123. A. Karbalayghareh, U.M. Braga-Neto, and E.R. Dougherty, “Classification of Single-cell Gene Expression Trajectories from Incomplete and Noisy Data,” *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, Vol. 16, No. 1, Jan-Feb 2019, pp. 193-207.

122. M. Imani and U.M. Braga-Neto, “Point-Based Methodology to Monitor and Control Gene Regulatory Networks via Noisy Measurements,” *IEEE Transactions on Control Systems Technology*, Vol. 23, No. 3, May 2019, pp. 1023-1035.

121. M. Imani and U.M. Braga-Neto, “Finite-Horizon LQR Controller for Partially-Observed Boolean Dynamical Systems,” *Automatica*, Vol. 95, p. 172-179, 2018.

120. K. Naragaja and U.M. Braga-Neto, “Bayesian Classification of Proteomics Biomarkers from Selected Reaction Monitoring Data using an ABC-MCMC Approach,” *Cancer Informatics*, Vol 17, Aug 2018.

119. M. Imani, R. Dehghannasiri, U.M. Braga-Neto and E.R. Dougherty, “Sequential Experimental Design for Optimal Structural Intervention in Gene Regulatory Networks Based on the Mean Objective Cost of Uncertainty, *Cancer Informatics*, Vol. 17, Aug 2018.

118. M. Imani and U.M. Braga-Neto, “Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs,” *IEEE Transactions on Control of Network Systems*, Vol. 5, Issue 2, Jun 2018, pp. 760-769.

117. M. Imani and U.M. Braga-Neto, ” Optimal Control of Gene Regulatory Networks with Unknown Cost Function,” *Proceedings of the 2018 American Control Conference (ACC’2018)*, Milwaukee, WI, June 2018.

116. M. Imani and U.M. Braga-Neto, “Gene regulatory network state estimation from arbitrary correlated measurements,” *EURASIP Journal on Advances in Signal Processing*, Vol. 2018, Apr 2018, p. 22.

115. A. Karbalayghareh, U.M. Braga-Neto, and E.R. Dougherty, “Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks,” *BMC Systems Biology*, Vol. 12 (Suppl 3), Mar 2018, p. 23.

114. A. Bahadorinejad and U.M. Braga-Neto, “Optimal Fault Detection and Diagnosis in Transcriptional Circuits using Next-Generation Sequencing,” *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, Vol. 15, Issue 2, Mar-Apr 2018.

113. M. Imani and U.M. Braga-Neto, “Particle Filters for Partially-Observed Boolean Dynamical Systems,” *Automatica*, Vol. 87, Jan 2018, pp 238-250.

112. L.D. Mcclenny, M. Imani and U. M Braga-Neto, “BoolFilter: an R package for estimation and identification of Partially-Observed Boolean Dynamical Systems,” *BMC Bioinformatics*, Vol. 87, Jan 2018, pp 238-250.

111. A. Karbalayghareh, U.M. Braga-Neto, J. Hua, and E.R. Dougherty, “Classification of State Trajectories in Gene Regulatory Networks,” *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, Vol. 15, Issue 1, Jan 2018, pp. 68-82.

110. E. Atashpaz-Gargari, M.S. Reis, U.M. Braga-Neto, J. Barrera and E.R. Dougherty, “A fast Branch-and-Bound algorithm for U-curve feature selection,” *Pattern Recognition*, Vol. 73, Jan 2018, pp. 172-188.

109. M. Imani and U.M. Braga-Neto, “Optimal Finite-Horizon Sensor Selection for Boolean Kalman Filter,” Proceedings of the 51th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October-November 2017.

108. S. Xie, M. Imani, E.R. Dougherty and U.M. Braga-Neto, “Nonstationary Linear Discriminant Analysis,” Proceedings of the 51th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October-November 2017.

107. E. Arslan and U.M. Braga-Neto, “Bayesian Top Scoring Pairs for Feature Selection,” Proceedings of the 51th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October-November 2017.

106. P.K. Riggs, L.O. Tedeschi, N.D. Turner, U. Braga-Neto, A. Jayaraman, “The role of ‘omics’ technologies for livestock sustainability,” Latin-American Archives of Animal Production, Vol. 25, 2017, pp. 147-153.

105. A. Karbalayghareh, U.M. Braga-Neto, and E.R. Dougherty, “Intrinsically Bayesian robust classifier for single-cell gene expression time series in gene regulatory networks,” Proceedings of the 4th International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2017), Boston, Aug 2017.

104. M. Imani and U.M. Braga-Neto, “Multiple Model Adaptive Controller for Partially-Observed Boolean Dynamical Systems,” Proceedings of the American Control Conference (ACC’2017), Seattle, WA May 2017. doi:10.23919/ACC.2017.7963100

103. R. Oliveira, M.T. Cordeiro, P. Moura, P. Baptista Filho, U.M. Braga-Neto, E.T.A. Marques Júnior, L.H.V.G. Gil, “Serum cytokine/chemokine profiles in patients with dengue fever (DF) and dengue hemorrhagic fever (FHD) by using protein array,” Journal of Clinical Virology, Vol. 89, Apr 2017, pp. 39-45.

102. E. Arslan and U.M. Braga-Neto, “A Bayesian Approach to Top-Scoring Pairs Classification,” Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’2017), New Orleans, LA, March 2017.

101. L.D. McClenny, M. Imani and U.M. Braga-Neto, “Boolean Kalman Filter with Correlated Observation Noise,” Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’2017), New Orleans, LA, March 2017.

100. A. Karbalayghareh, U.M. Braga-Neto, and E.R. Dougherty, “Classification of Gaussian Trajectories with Missing Data in Boolean Gene Regulatory Networks,” Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’2017), New Orleans, LA, March 2017.

99. M. Imani and U.M. Braga-Neto, “Maximum-Likelihood Adaptive Filter for Partially-Observed Boolean Dynamical Systems,” IEEE Transactions on Signal Processing, Vol. 65, No. 2, Jan 2017, pp. 359-371.

98. U. Banerjee and U.M. Braga-Neto, “Bayesian ABC-MCMC Classification of Liquid-Chromatography Mass Spectrometry Data,” Cancer Informatics, Vol. 2015(Supp 5), Jan 2017, pp. 175-182.

97. M. Imani and U.M. Braga-Neto, “Point-Based Value Iteration for Partially-Observed Boolean Dynamical Systems with Finite Observation Spaces,” Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, Dec 2016.

96. M. Imani and U.M. Braga-Neto, “State-Feedback Control of Partially-Observed Boolean Dynamical Systems Using RNA-Seq Time Series Data,” Proceedings of the 2016 American Control Conference, Boston, MA, July 2016.

95. T. Chen and U.M. Braga-Neto, “Bayesian Estimation of the Discrete Coefficient of Determination,” Special Issue on Bayesian Methods for Computational Systems Biology, EURASIP Journal of Bioinformatics and Systems Biology, 2016 Jan 15;2016(1):1.

94. M. Imani and U.M. Braga-Neto, “Optimal Gene Regulatory Network Inference using the Boolean Kalman Filter and Multiple Model Adaptive Estimation,” Proceedings of the 3rd IEEE Global Conference on Signal and Information Processing (GlobalSip’2015), Orlando, FL, December 2015.

93. M. Imani and U.M. Braga-Neto, “Optimal State Estimation for Boolean Dynamical Systems using a Boolean Kalman Smoother,” Proceedings of the 49th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2015. **Finalist Paper Award**

92. T. Chen and U.M. Braga-Neto, “Statistical Detection of Intrinsically Multivariate Predictive Genes,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 12, No. 4, Jul-Aug 2015, pp. 951-964.

91. U.M. Braga-Neto, A. Zollanvari, and E.R. Dougherty, “Cross-validation under separate sampling: strong bias and how to correct it,” Bioinformatics, Vol. 30, No. 23, Dec 1 2014, pp. 3349-3355.

90. A. Bahadorinejad and U.M. Braga-Neto, “Optimal Fault Detection in Stochastic Boolean Regulatory Networks,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2014), Atlanta, GA, December 2014.

89. X. Jiang and U.M. Braga-Neto, “A Naive-Bayes Approach to Bolstered Error Estimation in High-Dimensional Spaces,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2014), Atlanta, GA, December 2014.

88. B. Afsari, U.M. Braga-Neto, and D. Geman, “Rank Discriminants for Predicting Phenotypes from RNA Expression.” Annals of Applied Statistics, Vol. 8, No. 3, Sep 2014, pp. 1469-1491.

87. T. Chen and U.M. Braga-Neto, “Statistical Detection of Boolean Regulatory Relationships,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Sep-Oct 2013;10(5):1310-21, doi: 10.1109/TCBB.2013.118.

86. T.T. Vu, C. Sima, U.M. Braga-Neto, and E.R. Dougherty, “Unbiased Bootstrap Error Estimation for Linear Discriminant Analysis,” EURASIP Journal on Bioinformatics and Systems Biology, 2014, 2014:15.

85. E. Atashpaz-Gargari, U.M. Braga-Neto, and E.R. Dougherty, “Modeling and Systematic Analysis of Biomarker Validation using Selected Reaction Monitoring.” EURASIP Journal on Bioinformatics and Systems Biology, 2014, 2014:17.

84. U.M. Braga-Neto, “Particle Filtering Approach to State Estimation in Boolean Dynamical Systems,” Proceedings of IEEE GlobalSIP Symposium on Bioinformatics and Systems Biology, Austin, TX, December 2013.

83. T. Chen and U.M. Braga-Neto, “Optimal Bayesian MMSE Estimation of the Coefficient of Determination for Discrete Prediction,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2013), Houston, TX, November 2013.

82. A. Zollanvari, U.M. Braga-Neto and E.R. Dougherty, “Effect of Mixing Probabilities on the Bias of Cross-Validation Under Separate Sampling,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2013), Houston, TX, November 2013.

81. E. Atashpaz-Gargari, U.M. Braga-Neto and E.R. Dougherty, “Improved Branch-and-Bound Algorithm for U-Curve Optimization,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2013), Houston, TX, November 2013.

80. T. Chen and U.M. Braga-Neto, “Maximum-Likelihood Estimation of the Discrete Coefficient of Determination in Stochastic Boolean Systems,” IEEE Transactions on Signal Processing, Vol. 61, No. 15, August 1 2013, pp. 3880-3894.

79. A.F. Pastor, L. Rodrigues, J.W.D. Neto, E.J.M. Nascimento, C.E. Calzavara-Silva, A.L.V. Gomes, A.M. da Silva, M.T. Cordeiro, U.M. Braga-Neto, S. Crovella, L.H.V.G. Gil, B. Acioli-Santos, E.T.A. Marques Jr., “Complement factor H gene (CFH) polymorphisms C-257T, G257A and haplotypes are associated with protection against Severe Dengue Phenotype, possible related with high CFH expression,” Human Immunology, Vol. 74, No. 8, Sep 2013, pp. 1225-1230, doi:10.1016/j.humimm.2013.05.005. Epub 2013 Jun 6.

78. L.X.E. de Alencar, U.M. Braga-Neto, E.J.M. Nascimento, M.T. Cordeiro, A.M. Silva, C.A.A. de Britto, M.P.C. da Silva, L.H.V.G. Gil, S. Montenegro, E.T.A. Marques, “HLA-B*44 is Associated with Dengue Severity Caused by DENV-3 in a Brazilian Population,” Journal of Tropical Medicine. Volume 2013, Article ID 648475, 11 pages, 2013. doi:10.1155/2013/648475.

77. D.C. Martins, Jr., E.A. de Oliveira, U.M. Braga-Neto, R.F. Hashimoto and R.M. Cesar, Jr., “Signal Propagation in Bayesian Networks and its Relationship with Intrinsically Multivariate Predictive Variables,” Information Sciences, Vol. 225, Mar 2013, pp. 18-34, doi:10.1016/j.ins.2012.10.027.

76. A.B. Melo, E.J.M. Nascimento, U.M. Braga-Neto, R. Dhalia, A.M. Silva, M. Oelke, J.P. Schneck, J. Sidney, A. Sette, S. Montenegro, E.T.A. Marques, “T-Cell Memory Responses Elicited by Yellow Fever Vaccine are Targeted to Overlapping Epitopes Containing Multiple HLA-I and -II Binding Motifs,” PLoS Neglected Tropical Diseases, Vol. 7, No. 1, Jan 2013, p. e1938, doi:10.1371/journal.pntd.0001938.

75. T. Chen and U.M. Braga-Neto, “A Statistical Test for Intrinsically Multivariate Predictive Genes,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2012), Washington, DC, December 2012.

74. E. Atashpaz-Gargari, C. Sima, U.M. Braga-Neto and E.R. Dougherty, Relationship Between the Accuracy of Classifier Error Estimation and Complexity of the Decision Boundary,” Pattern Recognition, available online 6 Nov 2012, http://dx.doi.org/10.1016/j.patcog.2012.10.020.

73. U.M. Braga-Neto, “Joint State and Parameter Estimation for Boolean Dynamical Systems,” Proceedings of the IEEE Statistical Signal Processing Workshop, Ann Arbor, MI, August 2012.

72. Y. Sun, U.M. Braga-Neto and E.R. Dougherty, “Modeling and systematic analysis of the LC-MS proteomics pipeline,” BMC Genomics, 13(Suppl S6):S2, 2012.

71. C.C. Allen, B.R.C. Alves, X. Li, L.O. Tedeschi, H. Zhou, J.A. Paschal, P. Riggs, U.M. Braga-Neto, D.H. Keisler, G.L. Williams and M. Amstalden, “Gene expression in the arcuate nucleus of heifers is affected by controlled intake of high- and low-concentrate diets,” Journal of Animal Sciences, Vol. 90, No. 7, July 2012, pp. 2222-2232.

70. Y. Sun, J. Zhang, U.M. Braga-Neto and E.R. Dougherty, “BPDA2D — A 2D global optimization based Bayesian peptide detection algorithm for LC-MS,” Bioinformatics, 28(4): 564-572, 2012.

69. A. Zollanvari, U.M. Braga-Neto and E.R. Dougherty, “Exact representation of the second-order moments for resubstitution and leave-one-out error estimation for linear discriminant analysis in the univariate heteroskedastic Gaussian model,” Pattern Recognition, Vol. 45, No. 2, February 2012, pp. 908-917.

68. T. Chen and U.M. Braga-Neto, “Sample-Based Estimators for the Intrinsically Multivariate Prediction Score,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2011), San Antonio, TX, December 2011.

67. E. Atashpaz-Gargari, C. Sima, U.M. Braga-Neto and E.R. Dougherty, “Relationship Between the Accuracy of Classifier Error Estimation and Distribution Complexity,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2011), San Antonio, TX, December 2011.

66. S. Afra and U.M. Braga-Neto, “Peaking Phenomenon and Error Estimation for Support Vector Machines,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2011), San Antonio, TX, December 2011.

65. Y. Sun, U.M. Braga-Neto and E.R. Dougherty, “Modeling and systematic analysis of LC-MS proteomics pipeline,” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2011), San Antonio, TX, December 2011.

64. E. Atashpaz-Gargari, U.M. Braga-Neto and E.R. Dougherty, “Multiple Reaction Monitoring: Modeling and Systematic Analysis.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2011), San Antonio, TX, December 2011.

63. Z. Zhang, Y. Sun, U.M. Braga-Neto, E.R. Dougherty and J. Zhang, “A parallel programming framework with Markovian messaging for LCMS peptide peak detection.” Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM’2011), Atlanta, GA, November 2011.

62. U.M. Braga-Neto, “Optimal State Estimation for Boolean Dynamical Systems,” Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2011.

61. T. Chen and U.M. Braga-Neto, “Maximum Likelihood Estimation of the Binary Coefficient of Determination,” Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2011.

60. Y. Wang, N. Ghaffari, C.D. Johnson, U.M. Braga-Neto, H. Wang, R. Chen and H. Zhou, “Evaluation of coverage and depth of transcriptome by RNA-Seq in chickens,” BMC Bioinformatics, MCBIOS’2011 Special Issue, Vol. 12, Suppl. 10, October 2011, p. S5, doi:10.1186/1471-2105-12-S10-S5.

59. A.B. Melo, M. Silva, C. Magalhaes, L.H.V.G. Gil, E.M.F. Carvalho, G.R. Bertani, U.M. Braga-Neto, E.T.A. Marques and M.T. Cordeiro, “Description of a Prospective 17DD Yellow Fever Vaccinee Cohort in Recife, Brazil,” The American Journal of Tropical Medicine and Hygiene, Vol. 85, No. 4, October 2011, pp. 739-747.

58. A. Zollanvari, U.M. Braga-Neto and E.R. Dougherty, “Analytic Study of Performance of Error Estimators for Linear Discriminant Analysis,” IEEE Transactions on Signal Processing, Vol. 59, No. 9, September 2011, pp. 4238-4255.

57. C. Sima, T.T. Vu, U.M. Braga-Neto, and E.R. Dougherty, “High-Dimensional Bolstered Error Estimation,” Bioinformatics, Vol. 27, No. 21, September 2011, pp. 3056-3064.

56. E.R. Dougherty, A. Zollanvari and U.M. Braga-Neto, “The Illusion of Distribution-Free Small-Sample Classification in Genomics,” Current Genomics, Vol. 12, No. 5, August 2011, pp. 333-341.

55. Y. Sun, J. Zhang, U.M. Braga-Neto and E.R. Dougherty, “BPDA2D – An improved Bayesian peptide detection algorithm for Mass Spectrometry,” 59th ASMS Conference on Mass Spectrometry and Allied Topics, Denver, Colorado, June 2011.

54. T. Chen and U.M. Braga-Neto, “Approximate expressions for the variances of non-randomized error estimators and CoD estimators for the discrete histogram rule.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2010), Cold Spring Harbor, NY, November 2010.

53. A. Zollanvari, U.M. Braga-Neto, and E.R. Dougherty, “RMS bounds and sample size considerations for error estimation in linear discriminant analysis.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2010), Cold Spring Harbor, NY, November 2010.

52. T. Chen and U.M. Braga-Neto, “Exact Performance of CoD Estimators in Discrete Prediction,” EURASIP Journal on Advances in Signal Processing (JASP), Special Issue on Genomic Signal Processing, Volume 2010, Article ID 487893, 13 pages, 2010. doi:10.1155/2010/487893.

51. T.T. Vu and U.M. Braga-Neto, “Small-Sample Error Estimation for Bagged Classification Rules,” EURASIP Journal on Advances in Signal Processing (JASP), Special Issue on Genomic Signal Processing. Volume 2010, Article ID 548906, 12 pages, 2010. doi:10.1155/2010/548906.

50. Y. Sun, J. Zhang, U.M. Braga-Neto and E.R. Dougherty, “BPDA – a Bayesian peptide detection algorithm for mass spectrometry,” BMC Bioinformatics, Vol. 11, September 2010, p. 490, doi:10.1186/1471-2105-11-490.

49. A. Zollanvari, U.M. Braga-Neto and E.R. Dougherty, “Joint Sampling Distribution Between Actual and Estimated Classification Errors for Linear Discriminant Analysis,” IEEE Transactions on Information Theory, Vol. 56, No. 2, February 2010, pp. 784-804.

48. U.M. Braga-Neto and E.R. Dougherty, Exact Correlation between Actual and Estimated Errors in Discrete Classification, Pattern Recognition Letters, Vol. 31, No. 5, April 2010, pp. 407-412.

47. E.R. Dougherty, C. Sima, J. Hua, B. Hanczar and U.M. Braga-Neto, Performance of Error Estimators for Classification, Current Bioinformatics, Vol. 5, No. 1, March 2010, pp. 53-67.

46. Y. Sun, U.M. Braga-Neto and E.R. Dougherty, Impact of Missing Value Imputation on Classification for DNA Microarray Gene Expression Data – A Model-Based Study, EURASIP Journal on Bioinformatics and Systems Biology, Volume 2009, Article ID 504069, November 2009, 17 pages, doi:10.1155/2009/504069.

45. E.J.M. Nascimento, U.M. Braga-Neto, C. Calvazara, A.L. Gomes, F. Abath, B. Acioli, C.A.A. Brito, M.T. Cordeiro, A.M. Silva, C. Magalhaes, R. Andrade, L.H.V.G. Gil and E.T.A. Marques, Jr., Gene Expression Profiling During Acute Stage of Dengue Infection PLoS ONE, Vol. 4, No. 11, November 2009, p. e7892, doi:10.1371/journal.pone.0007892.

44. U.M. Braga-Neto, Classification and Error Estimation for Discrete Data, Current Genomics, Vol. 10, No. 7, November 2009, pp. 446-462.

43. A. Zollanvari, M.J. Cunningham, U.M. Braga-Neto and E.R. Dougherty, Analysis and Modeling of Time-Course Gene-Expression Profiles from Nanomaterial-Exposed Primary Human Epidermal Keratinocytes, BMC Bioinformatics, MCBIOS’2009 Special Issue, Vol. 10, Suppl 11, October 2009, p. S10.

42. E.J.M. Nascimento, A.M. Silva, M.T. Cordeiro, C.A.A. Brito, L.H.V.G. Gil, Ulisses Braga-Neto and E.T.A. Marques, Jr., Alternative Complement Pathway Deregulation Is Correlated with Dengue Severity, PLoS ONE, Vol. 4, No. 8, August 2009, p. e6782.

41. A. Zollanvari, U.M. Braga-Neto and E.R. Dougherty, On the Sampling Distribution of Resubstitution and Leave-One-Out Error Estimators for Linear Classifiers, Pattern Recognition, Vol. 42, No. 11, November 2009, pp. 2705-2723.

40. C. Allen, X. Li, L.O. Tedeschi, H. Zhou, J.A. Paschal, T.E. Spencer, U.M. Braga-Neto, D.H. Keisler, M. Amstalden and G.L. Williams, “Dietary Treatments That Facilitate Early Onset of Puberty in Heifers Alter Gene Expression in the Arcuate Nucleus,” Biology of Reproduction, Vol. 81 (Supp 1), p. 489, 2009.

39. T.T. Vu and U.M. Braga-Neto, Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data? EURASIP Journal on Bioinformatics and Systems Biology, Special Issue on Applications of Signal Processing Techniques to Bioinformatics, Genomics, and Proteomics, Volume 2009, Article ID 158368, 2009.

38. C. Sima, T.T. Vu, U.M. Braga-Neto, and E.R. Dougherty, “Bolstered Error Estimation with Feature Selection.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2009), Minneapolis, MN, May 2009.

37. A. Zollanvari, U.M. Braga-Neto, and E.R. Dougherty, “Sample Size Calculation from Specified RMS of the Resubstitution Error for Linear Classifiers.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2009), Minneapolis, MN, May 2009.

36. M.T. Cordeiro, U.M. Braga-Neto, R.M.R. Nogueira and E.T.A. Marques, Jr., Reliable classifier to differentiate primary and secondary acute dengue infection based on IgG ELISA, PLoS ONE, Vol. 4, No. 4, April 2009, p. e4945.

35. D.C. Martins, Jr., U.M. Braga-Neto, R.F. Hashimoto, M.L. Bittner and E.R. Dougherty, Intrinsically Multivariate Predictive Genes. IEEE Journal of Selected Topics in Signal Processing, Vol. 2, No. 3, June 2008, pp. 424-439.

34. U.M. Braga-Neto, “An Asymptotically-Exact Expression for the Variance of Classification Error for the Discrete Histogram Rule.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2008), Phoenix, AZ, June 2008.

33. D.C. Martins, Jr., U.M. Braga-Neto, M.L. Bittner and E.R. Dougherty, “Network Properties of Intrinsically Multivariate Predictive Genes.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2008), Phoenix, AZ, June 2008.

32. T.T. Vu, U.M. Braga-Neto and E.R. Dougherty, “Preliminary Study on Bolstered Error Estimation in High-Dimensional Spaces.” Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’2008), Phoenix, AZ, June 2008.

31. B. Acioli-Santos, L. Segat, R. Dhalia, C.A.A. Brito, U.M. Braga-Neto, E.T.A. Marques and S. Crovella, MBL2 Gene Polymorphisms Protect Against Development of Thrombocytopenia Associated with Severe Dengue Phenotype. Human Immunology, Vol. 69, No. 2, February 2008, pp. 122-128.

30. U.M. Braga-Neto, Fads and Fallacies in the Name of Small-Sample Microarray Classification. IEEE Signal Processing Magazine, Special Issue on Signal Processing Methods in Genomics and Proteomics, Vol. 24, No. 1, January 2007, pp. 91-99.

29. Q. Xu, J. Hua, U.M. Braga-Neto, Z. Xiong, E. Suh and E.R. Dougherty, Confidence Intervals for the True Classification Error Conditioned on the Estimated Error. Technology in Cancer Research and Treatment, Vol. 5, No. 6, December 2006, pp. 579-590.

28. U.M. Braga-Neto and E.A.T. Marques, Jr., From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards. PLoS Computational Biology, Vol. 2, No. 7, July 2006, p. e81.

27. E. Dougherty and U.M. Braga-Neto, Epistemology of Computational Biology: Mathematical Models and Experimental Prediction as the Basis of Their Validity. Journal of Biological Systems, Vol. 14, No. 1, March 2006, pp. 65-90.

26. C. Sima, S. Attoor, U.M. Braga-Neto, J. Lowey, E. Suh and E. Dougherty, Impact of Error Estimation on Feature-Selection Algorithms. Pattern Recognition, Vol. 38, No. 12, December 2005, pp. 2472-2482.

25. U.M. Braga-Neto and E. Dougherty, Exact Performance of Error Estimators for Discrete Classifiers. Pattern Recognition, Vol. 38, No. 11, November 2005, pp. 1799-1814.

24. U.M. Braga-Neto and J. Goutsias, Constructing Multiscale Connectivities. Computer Vision and Image Understanding, Vol. 99, No. 1, July 2005, pp. 126-150.

23. U.M. Braga-Neto and J. Goutsias, Object-Based Image Analysis Using Multiscale Connectivity. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 6, June 2005, pp. 892-907.

22. U.M. Braga-Neto, Multiscale Connected Operators. Journal of Mathematical Imaging and Vision (Special Issue on Mathematical Morphology After 40 Years), Vol. 22, No. 2-3, May 2005, pp. 199-216.

21. U.M. Braga-Neto, Small-Sample Error Estimation: Mythology Versus Mathematics. Proceedings of SPIE Vol. 5916: Mathematical Methods in Pattern and Image Analysis. Edited by J.T. Astola, I. Tabus and J. Barrera, San Diego, CA, August 2005.

20. U.M. Braga-Neto, Grayscale Level Multiconnectivity. Proceedings of the 7th International Symposium on Mathematical Morphology: 40 Years On – ISMM’05. Edited by C. Ronse, L. Najman and E. Decencière, pp. 129-138, Paris, France, April 2005.

19. C. Sima, U.M. Braga-Neto and E. Dougherty, Superior Feature-Set Ranking for Small Samples Using Bolstered Error Estimation. Bioinformatics, Vol. 21, No. 7, April 2005, pp. 1046-1054.

18. U.M. Braga-Neto and J. Goutsias, Grayscale Level Connectivity: Theory and Applications. IEEE Transactions on Image Processing, Vol. 13, No. 12, December 2004, pp. 1567-1580.

17. U.M. Braga-Neto and E. Dougherty, Bolstered Error Estimation. Pattern Recognition, Vol. 37, No. 6, June 2004, pp. 1267-1281.

16. U.M. Braga-Neto and E. Dougherty, Is Cross-Validation Valid for Small-Sample Microarray Classification? Bioinformatics, Vol. 20, No. 3, February 2004, pp. 374-380.

15. U.M. Braga-Neto, R. Hashimoto, E. Dougherty, D. Nguyen and R. Carroll, Is Cross-Validation Better Than Resubstitution for Ranking Genes? Bioinformatics, Vol. 20, No. 2, January 2004, pp. 253-258.

14. U.M. Braga-Neto, M. Choudhary and J. Goutsias, Automatic target detection and tracking on forward-looking infrared image sequences using morphological connected operators. Journal of Electronic Imaging, Vol. 13, No. 4, 2004, pp. 802-813.

13. U.M. Braga-Neto and J. Goutsias, Supremal Multiscale Signal Analysis. SIAM Journal of Mathematical Analysis, Vol. 36, No. 1, 2004, pp. 94-120.

12. U.M. Braga-Neto and J. Goutsias, A Theoretical Tour of Connectivity in Image Processing and Analysis. Journal of Mathematical Imaging and Vision, Vol. 19, No. 1, 2003, pp. 5-31.

11. U.M. Braga-Neto and J. Goutsias, A Multiscale Approach to Connectivity. Computer Vision and Image Understanding, Vol. 89, No. 1, 2003, pp. 70-107.

10. U.M. Braga-Neto and J. Goutsias, Connectivity on Complete Lattices: New Results. Computer Vision and Image Understanding, Vol. 85, No. 1, 2002, pp. 22-53.

9. X. Han, C. Xu, U.M. Braga-Neto and J.L. Prince, Topology Correction in Brain Cortex Segmentation Using a Multiscale, Graph-Based Algorithm. IEEE Transactions on Medical Imaging, Vol. 21, No. 2, 2002, pp. 109-121.

8. U.M. Braga-Neto and John Goutsias, On a General Theory of Connectivity in Image Analysis. Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing – NSIP’01. Edited by K. Barner, G.Arce, and C. Boncelet (CD-ROM), Baltimore, MD, June 2001.

7. X. Han, C. Xu, U.M. Braga-Neto, and J. L. Prince, Graph-Based Topology Correction for Brain Cortex Segmentation. Proceedings of the XVIIth International Conference on Information Processing in Medical Imaging – IPMI’01. Edited by M.F. Insana and R.M. Leahy, pp 395-401, Davis, CA, June 2001.

6. U.M. Braga-Neto and John Goutsias, Multiresolution Connectivity: An Axiomatic Approach. Proceedings of the 5th International Symposium on Mathematical Morphology and Its Applications to Image and Signal Processing – ISMM’2000. Edited by J. Goutsias, L. Vincent and D. Bloomberg, pp. 159-168, Palo Alto, CA, June 2000.

5. U.M. Braga-Neto and John Goutsias, On Detecting Mines and Minelike Objects in Highly Cluttered Multispectral Aerial Images by Means of Mathematical Morphology. In Detection and Remediation Technologies for Mines and Minelike Targets III, Proceedings of SPIE 3392, pp. 987-999, Orlando, FL, April 1998.

4. A.J. Candéas, U.M. Braga-Neto and E.C.B. Carvalho Filho, A Mathematical Morphology Approach to the Star/Galaxy Characterization Problem. Journal of the Brazilian Computer Society, Special Issue on Computer Graphics and Image Processing, Vol. 3, No. 3, 1997, pp. 14-29.

3. U.M. Braga-Neto, “Alternating Sequential Filters by Adaptive-Neighborhood Structuring Functions.” Proceedings of the 3rd International Symposium on Mathematical Morphology and Its Applications to Image and Signal Processing – ISMM’96. Edited by P. Maragos, R.W. Schafer and M.A. Butt, pp. 139-146, Atlanta, GA, May 1996.

2. U.M. Braga-Neto, W.A. Siqueira Neto and A.F. Dias e Silva, “Mammographic Calcification Detection by Mathematical Morphology Methods.” Proceedings of the 3rd International Workshop on Digital Mammography. Edited by K. Doi, M.L. Giger, R.M. Nishikawa and R.A. Schmidt, pp. 263-266, Chicago, IL, June 1996.

1. U.M. Braga-Neto and R.A. Lotufo, “Mathematical Morphology tools for 3-D image analysis of porous media.” In Neural, Morphological and Stochastic Methods in Image and Signal Processing, Proceedings of SPIE 2568. Edited by E.R. Dougherty, F. Prêteux and S.S. Shen, pp. 139-150, San Diego, CA, July 1995.

**Abstracts and Posters**

18. E. Arslan and U.M. Braga-Neto, “Bayesian method to determine informative genes in high-dimensional biological data,” 44th Annual Meeting of the Texas Genetics Society, College Station, TX, Apr 2017. [Best Poster Award]

17. X. Jiang, Y. Che, U.M. Braga-Neto and M. Dickman, “Identification of Biotic and Abiotic Stress Induced Pathways in Bananas,” Plant Biology 2016 Conference, Austin, July 2016.

16. M. Imani and U.M. Braga-Neto, “Adaptive Estimation and Control of Boolean Dynamical Systems,” Poster presented at the Eighth Annual Winedale Workshop, Winedale, TX, October 2015.

15. Y. Sun, J. Zhang, U.M. Braga-Neto and E.R. Dougherty, “BPDA2D – An improved Bayesian peptide detection algorithm for Mass Spectrometry,” 59th ASMS Conference on Mass Spectrometry and Allied Topics, Denver, Colorado, June 2011.

14. T. Chen and Ulisses Braga-Neto, “Maximum Likelihood Estimation of the Binary Coefficient of Determination,” Eighth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS’2011), College Station, TX, April 2011.

13. Y. Sun, J. Zhang, U.M. Braga-Neto and E.R. Dougherty, “BPDA+ – An improved Bayesian peptide detection algorithm for Mass Spectrometry,” Eighth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS’2011), College Station, TX, April 2011.

12. E. Atashpaz-Gargari, C. Sima, U.M. Braga-Neto and E.R. Dougherty, “Relationship Between the Accuracy of Classifier Error Estimation and Distribution Complexity,” Eighth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS’2011), College Station, TX, April 2011.

11. Y. Wang, N. Ghaffari, C.D. Johnson, U.M. Braga-Neto, H. Wang, R. Chen and H. Zhou, “Evaluation of Coverage and Depth of Transcriptome by RNA-SEQ In Chickens,” Eighth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS’2011), College Station, TX, April 2011.

10. C.C. Allen, X. Li, L.O. Tedeschi, H. Zhou, J.A. Paschal, T.E. Spencer, U.M. Braga-Neto, D.H. Keisler, M. Amstalden, and G.L. Williams, “Dietary Treatments that Facilitate Early Onset of Puberty in Heifers Alter Gene Expression in the Arcuate Nucleus.” Proceedings of the 42nd SSR Annual Meeting, Pittsburgh, PA, July 2009.

9. E. Nascimento, U.M. Braga-Neto, C. Calzavara-Silva, A. Gomes F. Abath, C Brito, M.Cordeiro, A. Silva, C. Magalhaes, R. Andrade, L. Gil and E.T.A. Marques, Jr., “Gene expression profiling during acute stage of dengue infection can predict patient outcome.” Proceedings of First Pan American Dengue Research Network Meeting, Recife, Brazil, July 2008.

8. E. Nascimento, A. Silva, B. Acioli-Silva, C. Calzavara-Silva, U.M. Braga-Neto, M. Cordeiro, C. Brito, M. Magalhaes, L. Gil and E.T.A. Marques, Jr., “Analysis of Complement System Reveals That Increased Alternative Pathway Activation Is correlated With Dengue Severity.” Proceedings of First Pan American Dengue Research Network Meeting, Recife, Brazil, July 2008.

7. B. Acioli-Silva, E. Nascimento, F. Pastor, C. Calzavara-Silva, A. Gomes, A. Silva, M. Cordeiro, U.M. Braga-Neto, S. Crovella and E.T.A. Marques, Jr., “Complement Factor H (CFH) Promoter Polymorphism C-257T is Correlated with High Levels of CFH mRNA and Protein Expression and Resistance to Dengue Hemorrhagic Fever.” Proceedings of First Pan American Dengue Research Network Meeting, Recife, Brazil, July 2008.

6. E. Nascimento, U.M. Braga-Neto, M. Magalhaes, C. Brito, M. Cordeiro, A. Silva and E.T.A. Marques, Jr., “HLA-C alleles are associated with resistance to sequential dengue infection and clinical outcomes.” Proceedings of First Pan American Dengue Research Network Meeting, Recife, Brazil, July 2008.

5. U.M. Braga-Neto, Error Estimation Critically Impacts “Feature Selection in Genomics and Proteomics Applications.” Presented at The Fifth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS 2008), Oklahoma City, OK, Feb 2008.

4. M. Pereira, A. Pastor, B. Acioli-Santos, L. Segat, R. Dhalia, U.M. Braga-Neto, E.T.A. Marques, Jr. and S. Crovella, “MBL2 gene polymorphisms protect against severe dengue manifestation associated with thrombocytopenia phenotype development.” Proceedings of XVIII Brazilian Virology Meeting, Buzios, Brazil, 2007.

3. L. Alencar, A. Barbosa, K. Barbosa, M. Tenorio, C. Brito, P. Chiklinkar, E. Nascimento, R. Dhalia, U.M. Braga-Neto, E.T.A. Marques, Jr., “Analysis of epitope mapping of the dengue-3 envelope protein on dengue patients: comparison between the enzyme-linked immunospot (elispot) and the multipred epitope predictive computational program.” Proceedings of XVII Brazilian Virology Meeting, Campos do Jordao, Brazil, 2006.

2. U.M. Braga-Neto, “Why Error Estimation is Fundamental to the Estimation of Regulatory Networks.” Presented at the Models for Genetic Regulatory Network Workshop, College Station, TX, November 2005.

1. U.M. Braga-Neto and John Goutsias, Automatic Target Detection and Tracking in Forward-Looking Infrared Image Sequences using Morphological Connected Operators. Proceedings of the33rd Annual Conference on Information Sciences and Systems – CISS’99. Vol. I, pp. 173-178, Baltimore, MD, March 1999.