Deep Learning for Healthcare Genomics

3 ラボ 4時 45分 60クレジット

In this hands-on course, you will learn the basics of deep learning and how to apply deep learning to detect chromosome co-deletion and search for motifs in genomic sequences. You will: ● Understand the basics of Convolutional Neural Networks (CNNs) and how they work ● Apply CNNs to MRI scans of LGGs to determine 1p/19q chromosome co-deletion status ● Use the DragoNN toolkit to simulate genomic data and to search for motifs Upon completion of this course, you’ll be able to: understand how CNNs work, evaluate MRI images using CNNs, and use real regulatory genomic data to research new motifs.

Quest Outline



ディープ・ラーニングとは、人間の視覚認識能力に近いマシンを提供するものであり、手作業でコーディングされたソフトウェアをデータから直接学習された予測モデルに置き換えることにより、多くのアプリケーションを変革させています。 このラボでは、機械学習のワークフローを紹介し、実世界の画像分類問題を解決するためにディープニューラルネットワーク(DNN)を使用するハンズオンを提供します。 データの準備、モデル定義、モデルの訓練とトラブルシューティング、検証テスト、およびモデルのパフォーマンスを向上させるための戦略について説明します。 また、モデルトレーニングプロセスのGPUによる高速化のメリットも確認​​できます。このラボを修了すると、NVIDIA DIGITSを使用して独自の画像分類データセットでDNNを学習する知識が得られます。

英語(English) 日本語 中国語(Chinese)

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.


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?

Enroll Now

Enroll in this quest to track your progress toward earning a badge.