Caicedo J, McQuin C, Goodman A, Singh S, Carpenter A. Weakly Supervised Learning of Single-Cell Feature Embeddings. Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018;2018:9309-9318. doi:10.1109/CVPR.2018.00970
Imaging Platform
Bray MA, Carpenter AE. Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler. Methods Mol Biol. 2018;1683:89-112. doi:10.1007/978-1-4939-7357-6_7
Becker T, Caicedo J, Singer S, weckmann M, AE C. Combining morphological and migration profiles of in vitro time-lapse data. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). ; 2018. doi:10.1109/ISBI.2018.8363731
Bray MA, Gustafsdottir SM, Rohban MH, et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience. 2017;6(12):1-5. doi:10.1093/gigascience/giw014
Eulenberg P, Köhler N, Blasi T, et al. Reconstructing cell cycle and disease progression using deep learning. Nat Commun. 2017;8(1):463. doi:10.1038/s41467-017-00623-3
Caicedo JC, Cooper S, Heigwer F, et al. Data-analysis strategies for image-based cell profiling. Nat Methods. 2017;14(9):849-863. doi:10.1038/nmeth.4397
Hulshof FFB, Papenburg B, Vasilevich A, et al. Mining for osteogenic surface topographies: In silico design to in vivo osseo-integration. Biomaterials. 2017;137:49-60. doi:10.1016/j.biomaterials.2017.05.020
Barczak AK, Avraham R, Singh S, et al. Systematic, multiparametric analysis of Mycobacterium tuberculosis intracellular infection offers insight into coordinated virulence. PLoS Pathog. 2017;13(5):e1006363. doi:10.1371/journal.ppat.1006363
Rohban MH, Singh S, Wu X, et al. Systematic morphological profiling of human gene and allele function via Cell Painting. Elife. 2017;6. doi:10.7554/eLife.24060
Goldsborough P, Pawlowski N, Caicedo J, Singh S, Carpenter A. CytoGAN: Generative Modeling of Cell Images. In: Workshop on Machine Learning in Computational Biology, Neural Information Processing Systems. . doi:https://doi.org/10.1101/227645