|
Link to all publications:
|
|
Selected Publications/PREPRINTs
csbconsortium.org/wp-content/uploads/2018/09/Screen-Shot-2019-10-09-at-11.16.18-AM.pngChen X, Fan L, Park CY, Friend L, Rampersaud S, Plitas G, Rudensky Y, Azizi E. Semi-supervised Deconvolution of Spatial Transcriptomics in Breast Tumors, Workshop on Computational Biology at ICML, 2021.
Jin Y*, Toberoff A*, Azizi E, Transfer learning framework for cell segmentation with incorporation of geometric features, LMRL Workshop at NeurIPS, BioRxiv preprint, 2020.
Bachireddy P*^, Azizi E*^, Burdziak C, Nguyen VN, Ennis C, Choo Z-N, Li S, Livak K, Neuberg DS, Soiffer RJ, Ritz J, Alyea E, Pe'er D^, Wu CJ^. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy. BioRxiv preprint, 2020.
Burdziak C*, Azizi E*, Prabhakaran S, & Pe'er D, A Nonparametric Multi-view model for Estimating Cell Type-Specific Gene Regulatory Networks, arXiv 1902.08138, 2019.
Azizi E*, Carr AJ*, Plitas G*, Cornish AE*, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme, R.M., Dao P, McKenney P.T., Wasti, R.C., Kadaveru, K., Mazutis L, Rudensky AY^, Pe'er D^, Single-cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment, Cell 174 (5): 1293-1308, 2018 (Featured as Cover Story) (outreach piece by NCI Cancer Systems Biology Consortium)
Azizi E*, Prabhakaran* S, Carr A, Pe'er D, Bayesian Inference for Single-cell Clustering and Imputing, Genomics and Computational Biology 3 (1), 46, 2017.
Prabhakaran S*, Azizi E*, Carr A, Pe'er D, Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data, Proceedings of The 33rd International Conference on Machine Learning (ICML), PMLR 48:1070-1079, 2016 (Acceptance rate: 24%) (R code) (Recipient of Dataminr Poster Presentation Award, NYAS Machine Learning Symposium 2016).
Azizi E, Airoldi EM, Galagan JE, Learning Modular Structures from Network Data and Node Variables, Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2):1440-1448, 2014 (Acceptance rate: 22%)(Recipient of IBM Best Student Paper Award, NESS 2014) (Extended version).
Galagan JE, Minch* K, Peterson* M, Lyubetskaya* A, Azizi* E, Sweet* L, Gomes* A, Rustad T, Dolganov G, Glotova I, Abeel T, Mahwinney C, Kennedy AD, Allard R, Brabant W, Krueger A, Jaini S, Honda B, Yu WH, Hickey MJ, Zucker J, Garay C, Weiner B, Sisk P, Stolte C, Winkler JK, Van de Peer Y, Iazzetti P, Camacho D, Dreyfuss J, Liu Y, Dorhoi A, Mollenkopf HJ, Drogaris P, Lamontagne J, Zhou Y, Piquenot J, Park ST, Raman S, Kaufmann SH, Mohney RP, Chelsky D, Moody DB, Sherman DR, Schoolnik GK, The Mycobacterium tuberculosis regulatory network and hypoxia, Nature. 2013 Jul 11; 499 (7457): 178-183. doi: 10.1038/nature12337 (pdf)
* denotes equal contributions
^ denotes co-corresponding
Jin Y*, Toberoff A*, Azizi E, Transfer learning framework for cell segmentation with incorporation of geometric features, LMRL Workshop at NeurIPS, BioRxiv preprint, 2020.
Bachireddy P*^, Azizi E*^, Burdziak C, Nguyen VN, Ennis C, Choo Z-N, Li S, Livak K, Neuberg DS, Soiffer RJ, Ritz J, Alyea E, Pe'er D^, Wu CJ^. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy. BioRxiv preprint, 2020.
Burdziak C*, Azizi E*, Prabhakaran S, & Pe'er D, A Nonparametric Multi-view model for Estimating Cell Type-Specific Gene Regulatory Networks, arXiv 1902.08138, 2019.
Azizi E*, Carr AJ*, Plitas G*, Cornish AE*, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme, R.M., Dao P, McKenney P.T., Wasti, R.C., Kadaveru, K., Mazutis L, Rudensky AY^, Pe'er D^, Single-cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment, Cell 174 (5): 1293-1308, 2018 (Featured as Cover Story) (outreach piece by NCI Cancer Systems Biology Consortium)
Azizi E*, Prabhakaran* S, Carr A, Pe'er D, Bayesian Inference for Single-cell Clustering and Imputing, Genomics and Computational Biology 3 (1), 46, 2017.
Prabhakaran S*, Azizi E*, Carr A, Pe'er D, Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data, Proceedings of The 33rd International Conference on Machine Learning (ICML), PMLR 48:1070-1079, 2016 (Acceptance rate: 24%) (R code) (Recipient of Dataminr Poster Presentation Award, NYAS Machine Learning Symposium 2016).
Azizi E, Airoldi EM, Galagan JE, Learning Modular Structures from Network Data and Node Variables, Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2):1440-1448, 2014 (Acceptance rate: 22%)(Recipient of IBM Best Student Paper Award, NESS 2014) (Extended version).
Galagan JE, Minch* K, Peterson* M, Lyubetskaya* A, Azizi* E, Sweet* L, Gomes* A, Rustad T, Dolganov G, Glotova I, Abeel T, Mahwinney C, Kennedy AD, Allard R, Brabant W, Krueger A, Jaini S, Honda B, Yu WH, Hickey MJ, Zucker J, Garay C, Weiner B, Sisk P, Stolte C, Winkler JK, Van de Peer Y, Iazzetti P, Camacho D, Dreyfuss J, Liu Y, Dorhoi A, Mollenkopf HJ, Drogaris P, Lamontagne J, Zhou Y, Piquenot J, Park ST, Raman S, Kaufmann SH, Mohney RP, Chelsky D, Moody DB, Sherman DR, Schoolnik GK, The Mycobacterium tuberculosis regulatory network and hypoxia, Nature. 2013 Jul 11; 499 (7457): 178-183. doi: 10.1038/nature12337 (pdf)
* denotes equal contributions
^ denotes co-corresponding
