Deep Representation Learning for Image-Based Cell Profiling

[1]  Casey S. Greene,et al.  Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders , 2017, bioRxiv.

[2]  Lassi Paavolainen,et al.  Data-analysis strategies for image-based cell profiling , 2017, Nature Methods.

[3]  H. Akaike A new look at the statistical model identification , 1974 .

[4]  Anne E Carpenter,et al.  Annotated high-throughput microscopy image sets for validation , 2012, Nature Methods.

[5]  R. Durbin,et al.  Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes , 2010, Nature.

[6]  Huachun Tan,et al.  Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering , 2016, IJCAI.

[7]  Anne E Carpenter,et al.  Automating Morphological Profiling with Generic Deep Convolutional Networks , 2016, bioRxiv.

[8]  Michael R Berthold,et al.  KNIME for reproducible cross-domain analysis of life science data. , 2017, Journal of biotechnology.

[9]  Anne E Carpenter,et al.  Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment , 2013, Journal of biomolecular screening.

[10]  Johannes E. Schindelin,et al.  The ImageJ ecosystem: An open platform for biomedical image analysis , 2015, Molecular reproduction and development.

[11]  Sriram Vishwanath,et al.  Learning Representations by Maximizing Mutual Information in Variational Autoencoders , 2019, 2020 IEEE International Symposium on Information Theory (ISIT).

[12]  Tomasz Bednarz,et al.  Visual analytics of single cell microscopy data using a collaborative immersive environment , 2018, VRCAI.

[13]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..

[15]  Jacob C. Kimmel Disentangling latent representations of single cell RNA-seq experiments , 2020, bioRxiv.

[16]  Xian Zhang,et al.  A multi‐scale convolutional neural network for phenotyping high‐content cellular images , 2017, Bioinform..

[17]  Anne E Carpenter,et al.  CellProfiler 3.0: Next-generation image processing for biology , 2018, PLoS biology.

[18]  Fred A. Hamprecht,et al.  ilastik: interactive machine learning for (bio)image analysis , 2019, Nature Methods.

[19]  M. Plummer,et al.  A Bayesian information criterion for singular models , 2013, 1309.0911.