Bayesian multiple instance regression for modeling immunogenic neoantigens
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Tao Wang | Guanghua Xiao | Xinlei Wang | Seongoh Park | Johan Lim | Tianshi Lu | Johan Lim | Guanghua Xiao | Xinlei Wang | Seongoh Park | Tianshi Lu | Tao Wang
[1] F. Rodríguez,et al. Immunodominance in Virus-Induced CD8+ T-Cell Responses Is Dramatically Modified by DNA Immunization and Is Regulated by Gamma Interferon , 2002, Journal of Virology.
[2] Zoran Obradovic,et al. Aerosol Optical Depth Prediction from Satellite Obsercations by Multiple Instance Regression , 2008, SDM.
[3] Arnold Zellner,et al. Applications of Bayesian Analysis in Econometrics , 1983 .
[4] David Page,et al. Multiple Instance Regression , 2001, ICML.
[5] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[6] J. Gartner,et al. Immunogenicity of somatic mutations in human gastrointestinal cancers , 2015, Science.
[7] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[8] Yixin Chen,et al. MILES: Multiple-Instance Learning via Embedded Instance Selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Xiaoxiao Du,et al. Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[10] Mark W. Ball,et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma , 2018, Science.
[11] Nicolai J. Birkbak,et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. , 2017, The Lancet. Oncology.
[12] Kiri L. Wagstaff,et al. Salience Assignment for Multiple-Instance Regression , 2007 .
[13] A. V. D. Vaart,et al. BAYESIAN LINEAR REGRESSION WITH SPARSE PRIORS , 2014, 1403.0735.
[14] R. Inman,et al. Immunodominance: a pivotal principle in host response to viral infections. , 2012, Clinical immunology.
[15] Morten Nielsen,et al. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction , 2007, BMC Bioinformatics.
[16] Alessandro Sette,et al. Properties of MHC Class I Presented Peptides That Enhance Immunogenicity , 2013, PLoS Comput. Biol..
[17] Jinbo Bi,et al. Effective 3D object detection and regression using probabilistic segmentation features in CT images , 2011, CVPR 2011.
[18] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[19] O. Lund,et al. NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence , 2007, PloS one.
[20] Vasant Honavar,et al. Predicting MHC-II Binding Affinity Using Multiple Instance Regression , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[21] Sri Krishna,et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes , 2015, Proceedings of the National Academy of Sciences.
[22] Svetha Venkatesh,et al. Bayesian nonparametric Multiple Instance Regression , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[23] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[24] Eric Granger,et al. Multiple instance learning: A survey of problem characteristics and applications , 2016, Pattern Recognit..
[25] K. Cibulskis,et al. Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia. , 2014, Blood.
[26] G. Linette,et al. Neoantigen Vaccines Pass the Immunogenicity Test. , 2017, Trends in molecular medicine.
[27] Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes , 2020, Science Immunology.
[28] James R. Foulds,et al. A review of multi-instance learning assumptions , 2010, The Knowledge Engineering Review.
[29] J. Yewdell,et al. Immunodominance in TCD8+ responses to viruses: cell biology, cellular immunology, and mathematical models. , 2004, Immunity.
[30] E. George,et al. Journal of the American Statistical Association is currently published by American Statistical Association. , 2007 .
[31] T. J. Mitchell,et al. Bayesian Variable Selection in Linear Regression , 1988 .
[32] Steven J. M. Jones,et al. Comprehensive molecular characterization of clear cell renal cell carcinoma , 2013, Nature.
[33] Jesse Davis,et al. An integrated approach to feature invention and model construction for drug activity prediction , 2007, ICML '07.
[34] Marco Loog,et al. Multiple instance learning with bag dissimilarities , 2013, Pattern Recognit..
[35] Andrei Popescu-Belis,et al. Explicit Document Modeling through Weighted Multiple-Instance Learning , 2017, J. Artif. Intell. Res..
[36] E. Mardis,et al. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells , 2015, Science.
[37] L. Wasserman,et al. A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion , 1995 .
[38] H. Aburatani,et al. Integrated molecular analysis of clear-cell renal cell carcinoma , 2013, Nature Genetics.
[39] A. Levine,et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy , 2017, Nature.
[40] Leonard D. Goldstein,et al. An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors. , 2018, Cancer discovery.
[41] Yang Xie,et al. Artificial Intelligence in Lung Cancer Pathology Image Analysis , 2019, Cancers.
[42] Aki Vehtari,et al. Sparsity information and regularization in the horseshoe and other shrinkage priors , 2017, 1707.01694.
[43] Andrei Popescu-Belis,et al. Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis , 2014, EMNLP.
[44] James G. Scott,et al. Handling Sparsity via the Horseshoe , 2009, AISTATS.
[45] Steven J. M. Jones,et al. Comprehensive molecular characterization of clear cell renal cell carcinoma , 2013, Nature.
[46] T. Schumacher,et al. Neoantigen landscape dynamics during human melanoma–T cell interactions , 2016, Nature.