GLBIO 2016 machine learning workshop

Outline

This workshop covers biological applications of dimensionality reduction, clustering, and supervised learning.

Each topic is presented in its own Jupyter notebook.

1. Supervised learning
2. Dimensionality reduction
3. Clustering

Course slides

Some slides are integrated into the Jupyter notebooks. External slides are available here:

• Introduction
• Supervised learning
• Clustering

Prerequisites

Suggested installation

1. Install the Anaconda Python distribution. Use the Python 3.5 version.

2. Install Plotly. On Mac, open Terminal through Finder. On Windows, open Anaconda Prompt. Run this command:

     pip install plotly
   

Manual installation (advanced users)

If you're an advanced user who is not using Anaconda, install the following packages:

• jupyter
• pandas
• plotly
• scikit-learn

Instructors

• Ivan Kryukov is a PhD student at the University of Calgary in the de Koning lab. Ivan's research is on inferring patterns of selection using comparative and population data.

• Jeff Wintersinger is a PhD student at the University of Toronto in the Morris lab. Jeff's research focuses on tumor heterogeneity and cancer evolution.

• Shahin Mohammadi is a PhD candidate at Purdue. Shahin was gracious in providing advice on the workshop throughout its development.

Helpers

• Nathan Bryans is a MSc student at the University of Calgary in the de Koning lab.

• Te Chen is an undergraduate student at the University of Toronto in the Morris lab. Te’s research focuses on understanding stem cell development using statistical methods.

• Chris Cremer is a PhD student at the University of Toronto in the Morris lab. Chris' research focuses on tumour deconvolution and protein structure prediction.

• Florian Goebels is a postdoc at the University of Toronto in the Bader lab. Florian's research is on protein-protein interactions.

• Kevin Ha is a PhD student at the University of Toronto in the Morris and Biencowe labs. Kevin's research focuses on 3'-end mRNA processing and alternative splicing.

• Nil Sahin is a PhD student at the University of Toronto in the Andrews and Morris labs. Nil's research focuses on modelling yeast microscopy images.

• Ahmad Shah is a bioinformatician at the University of Toronto in the Bader lab. Ahmad's research focuses on identifying regulatory regions and and exploring non-coding regions using machine learning.