Workshop Description

Machine learning and deep learning has been applied to various problems in genomics and biology, including DNA sequence modeling, gene expression analysis, drug discovery, and protein structure prediction. One of the key aspects of successful application of deep learning in these fields is the proper selection of neural network architecture through automated machine learning (AutoML).

AutoML refers to the process of automatically searching for the best neural network architecture, and more broadly, hyperparameter tuning, model selection and data preprocessing. These techniques are crucial for achieving high performance, while properly leveraging them can significantly improve the generalization of the models and ease the model building process.

In this workshop, we will cover the basics of applying a convolutional neural network (CNN) to model genomic sequences. Building on top of this, we will explore how different network architectures and hyperparameters affect the model’s performance. In the second half of the workshop, we will introduce how this manual tuning of models can be replaced with a Python package. We wrap up the tutorial by interpreting the biological insights we can learn from this CNN model.

Workshop Materials

Day 1:

Building CNNs

– Understand how CNNs model genomic sequences.

– Build CNNs with keras and pytorch.

– Model evaluations and tuning.

Day 2: AutoML

– Introduction of reinforcement learning.

– Apply NAS to automate CNN tuning.

– Interpret model and sequence motifs.

Technical Requirements

A computer with access to Google Colaboratory.


Dr. Frank Zijun Zhang is a faculty member in the Division of Artificial Intelligence in Medicine at the Cedars-Sinai Medical Center. Previously, he was a Flatiron Research fellow with Dr. Olga Troyanskaya at Princeton University and the Simons Foundation. He obtained his Ph.D. in Bioinformatics and a Master’s degree in Statistics at UCLA, with research focused on transcriptome analysis and automated machine learning and deep learning methods.




Workshop Details


W17: Machine Learning w/ Python is recommended

Basic Knowledge on Python and Jupyter notebook and/or Google Colaboratory

Length: 2 days, 3 hrs per day
Level: Intermediate
Location: Boyer 529
Seats Available: 28

Winter 2023 Dates

Jan 31, and Feb. 1
1:30 PM – 4:30 PM