Subgroup O

Subgroup O: Machine Learning in Cell Biology

Saturday, December 8, 1:30 pm-5:30 pm
Room: 31C

Organizer: Kwonmoo Lee, Worcester Polytechnic Institute

Quantitative cell biology underwent dramatic growth over the last decade due to a wide range of image analysis algorithms as well as advanced microscopy. This enables researchers to quantitatively measure cellular and subcellular phenomena in unpreceded detail, and build various datasets of cell biological processes, including cell motility, cytoskeleton, and membrane-bound organelles. Recently, machine learning has been making tremendous progress and has shown that computers can outperform humans in the analysis of complex high dimensional datasets. Thus, machine learning has great potential to extract hidden information from heterogeneous cell image datasets and provide detailed mechanistic biological insights. The session will showcase exciting applications of machine learning in various cell biological problems and present novel machine learning techniques that can be applied in cellular image analysis.


1:30 pm          Introduction. Kwonmoo Lee, Worcester Polytechnic Institute

1:35 pm          Machine learning of the assembly instructions of a cell. Robert Murphy, Carnegie Mellon University

2:00 pm          Inferring cell state dynamics with machine learning methods. Jacob Kimmel, Calico Life Sciences and University of California, San Francisco

2:25 pm          Deconvolution of subcellular protrusion heterogeneity by machine learning-based live cell analysis. Kwonmoo Lee, Worcester Polytechnic Institute

2:50 pm          A machine learning framework for modeling the structure and function of a cell. Trey Ideker, University of California, San Diego

3:15 pm          Break

3:25 pm          Machine learning for cell organization: new methods to capture variation and integrate observations. Greg Johnson, Allen Institute for Cell Science

3:50 pm          High-resolution characterization of complex organelle morphology using deep convolutional networks. Ge Yang, Carnegie Mellon University

4:15 pm          Machine learning and computer vision approaches for phenotypic profiling in yeast. Brenda Andrews, University of Toronto

4:40 pm          Live cell histology for classification of melanoma cell population based on single cell actions. Assaf Zaritsky, Ben-Gurion University of the Negev and University of Texas Southwestern Medical Center

5:05 pm          Context-aware predictions for tracking and segmentation. Jan Funke, Howard Hughes Medical Institute Janelia Farm


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