Learn how to apply convolutional neural networks (CNNs) to detect chromosome co-deletion and search for motifs in genomic sequences.
This course teaches you how to apply deep learning to detect chromosome co-deletion and search for motifs in genomic sequences. You’ll learn how to:
• Understand the basics of convolutional neural networks (CNNs) and how they work
• Apply CNNs to MRI scans of low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status
• Use the DragoNN toolkit to simulate genomic data and to search for motifs
08:30 Registration
09:00 Welcome
09:15 Image Classification with Digits
10.30 Morning Break
11.00 Image Classification with Digits (Continued)
11.45 Deep Learning for Genomics using DragoNN with Keras and Theano
12:30 Lunch
13:30 Deep Learning for Genomics using DragoNN with Keras and Theano (Continued)
14:45 Radiomics 1p19q Chromosome Image Classification with TensorFlow
15:30 Afternoon Break
16:00 Radiomics 1p19q Chromosome Image Classification with TensorFlow (Continued)
17:15 Closing Comments and Questions
LAB #1: IMAGE CLASSIFICATION WITH DIGITS (120MINS)
Learn to interpret deep learning models to discover predictive genome sequence patterns using the DragoNN toolkit on simulated and real regulatory genomic data.
LAB #2: DEEP LEARNING FOR GENOMICS USING DRAGONN WITH KERAS AND THEANO (120 MINS)
Learn to interpret deep learning models to discover predictive genome sequence patterns using the DragoNN toolkit on simulated and real regulatory genomic data.
LAB #3: RADIOMICS 1P19Q CHROMOSOME IMAGE CLASSIFICATION WITH TENSORFLOW (120 MINS)
Learn how to apply deep learning techniques to detect the 1p19q co-deletion biomarker from MRI imaging.
PRE-REQUISITES
Basic familiarity with deep neural networks, and basic coding experience in Python or a similar language.