Deep Learning for Healthcare Genomics

3 Labs · 60 Credits · 4h 43m

Deep Learning Badge nvidia intro dl

Learn the basics of deep learning and how to apply deep learning to detect chromosome co-deletion and search for motifs in genomic sequences. Upon completion of this course, you’ll understand how CNNs work, evaluate MRI images using CNNs, and use real regulatory genomic data to research new motifs.

Image Classification with DIGITS

Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognize handwritten digits by:

  • Loading image data to a training environment
  • Choosing and training a network
  • Testing with new data and iterating to improve performance

  • On completion of this Lab, you will be able to assess what data you should be training from.

    Icon  intro introductory Free 1 Hour 30 Minutes

    Image Classification with TensorFlow: Radiomics - 1p19q Chromosome Status Classification

    Thanks to work being performed at Mayo Clinic, approaches using deep learning techniques to detect Radiomics from MRI imaging can lead to more effective treatments and yield better health outcomes for patients with brain tumors. Radiogenomics, specifically Imaging Genomics, refers to the correlation between cancer imaging features and gene expression. Imaging Genomics (Radiomics) can be used to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy. The focus of this lab is detection of the 1p19q co-deletion biomarker using deep learning - specifically convolutional neural networks – using Keras and TensorFlow. What is remarkable about this research and lab is the novelty and promising results of utilizing deep learning to predict Radiomics.

    Icon  level Intermediate 30 Credits 1 Hour 30 Minutes

    Deep Learning for Genomics using DragoNN with Keras and Theano

    In this lab, we use the DragoNN toolkit on simulated and real regulatory genomic data, demystify popular DragoNN (Deep RegulAtory GenOmics Neural Network) architectures and provide guidelines for modeling and interpreting regulatory sequence using DragoNN models. We will answer questions such as When is a DragoNN good choice for a learning problem in genomics? How does one design a high-performance model? And more importantly, can we interpret these models to discover predictive genome sequence patterns to gain new biological insights?

    Icon  intro introductory 30 Credits 1 Hour 30 Minutes