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Deep Learning for Cytometry Data In-Person
Overview
Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. In this workshop, we will build and test a deep convolutional neural network to diagnose the latent cytomegalovirus (CMV) in healthy individuals using CyTOF data. In addition, we will developed a permutation-based method for interpreting the deep convolutional neural network model and identify key immune cells associated with the CMV infection.
Learning Objectives
- Understand basic concepts in neural networks
- Learn to build and train neural networks using Keras
- Design customized deep learning models for cytometry data (CyTOF and flow cytometry data)
- Apply techniques to interpret deep learning models
Prerequisites / Preparation
Knowledge of several concepts in machine learning: logistic regression, cross-validation, ROC curves, and decision trees. Familiarity with Jupyter notebooks and basic Python 3 data structures including: dictionary, NumPy arrays, and pandas data frames will help if you would like to follow the code examples after the workshop (see the materials section below).
Software
This workshop will be a demonstration of how to use Python and Jupyter notebooks to perform deep learning. If you would like to install software that will allow you to run the code on your own, installation instructions are linked below. You will not need these during the actual workshop.
Materials
Workshop materials are available here.
Instructors
Zicheng Hu is a Research Scientist in the Butte Lab at the Bakar Computational Health Sciences Institute at UCSF.
Sanchita Bhattacharya is a Bioinformatics Project Leader in the Butte Lab at the Bakar Computational Health Sciences Institute at UCSF.
Related LibGuide: Bioinformatics and Statistics Resources by Ariel Deardorff
- Date:
- Thursday, Apr 30 2020
- Time:
- 1:00pm - 4:00pm
- Time Zone:
- Pacific Time - US & Canada (change)
- Campus:
- Online
- Categories:
- Data Science > Bioinformatics and Statistics Data Science Data Science > Programming