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PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning.

P. Charoenkwan, C. Pipattanaboon, C. Nantasenamat, M. Hasan, M. Moni, P. Lio', and W. Shoombuatong. Comput. Biol. Medicine, (2023)

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PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning.P. Charoenkwan, C. Pipattanaboon, C. Nantasenamat, M. Hasan, M. Moni, P. Lio', and W. Shoombuatong. Comput. Biol. Medicine, (2023)Prediction of Mouse Senescence from HE-Stain Liver Images Using an Ensemble SVM Classifier.H. Huang, M. Hsu, H. Lee, P. Charoenkwan, S. Ho, and S. Ho. ACIIDS (2), volume 7803 of Lecture Notes in Computer Science, page 325-334. Springer, (2013)NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides.P. Charoenkwan, N. Schaduangrat, P. Lio', M. Moni, B. Manavalan, and W. Shoombuatong. Comput. Biol. Medicine, (2022)PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning.P. Charoenkwan, P. Chumnanpuen, N. Schaduangrat, C. Oh, B. Manavalan, and W. Shoombuatong. Comput. Biol. Medicine, (May 2023)Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.P. Charoenkwan, C. Nantasenamat, M. Hasan, and W. Shoombuatong. J. Comput. Aided Mol. Des., 34 (10): 1105-1116 (2020)HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.P. Charoenkwan, E. Hwang, R. Cutler, H. Lee, L. Ko, H. Huang, and S. Ho. BMC Bioinform., 14 (S-16): S12 (2013)SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins.P. Charoenkwan, N. Schaduangrat, M. Moni, P. Lio', B. Manavalan, and W. Shoombuatong. Comput. Biol. Medicine, (2022)Designing predictors of halophilic and non-halophilic proteins using support vector machines.H. Huang, Y. Srinivasulu, P. Charoenkwan, H. Lee, and S. Ho. CIBCB, page 230-237. IEEE, (2013)Predicting protein crystallization using a simple scoring card method.W. Shoombuatong, H. Huang, J. Chaijaruwanich, P. Charoenkwan, H. Lee, and S. Ho. CIBCB, page 23-30. IEEE, (2013)DNA Microarray Data Clustering by Hidden Markov Models and Bayesian Information Criterion.P. Charoenkwan, A. Manorat, J. Chaijaruwanich, S. Prasitwattanaseree, and S. Bhumiratana. ADMA, volume 4093 of Lecture Notes in Computer Science, page 827-834. Springer, (2006)

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PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning.

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PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning.