Machine Learning in Genomics

Subject Area
Biology, Bioinformatics

This is for you if

You are interested in biology and genomics
You are fascinated about using machine learning to solve real-world problems
You are excited to use data to make impactful predictions in biology
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What will you learn?

What machine learning is and its various categories
Understand classical machine learning techniques for classification and regression
How to apply standard machine learning algorithms to a real dataset

What will you create?

A 4-page research proposal on what you want to explore using machine learning and what interests you
View details
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Meet the Instructor

Ph.D., Neuroscience, Emory University (expected 2024)
B.S., Neuroscience, Brown University

The instructor’s research revolves around using machine learning and statistical approaches to derive insights into large genomic and neurogenetic datasets. He has distinguished himself as an award-winning teaching assistant across numerous undergraduate- and graduate-level courses at Brown University and Emory University.

Ph.D., Neuroscience, Emory University (expected 2024)
B.S., Neuroscience, Brown University

The instructor’s research revolves around using machine learning and statistical approaches to derive insights into large genomic and neurogenetic datasets. He has distinguished himself as an award-winning teaching assistant across numerous undergraduate- and graduate-level courses at Brown University and Emory University.

What’s Next?

This workshop
Machine Learning in Genomics
In-depth Research
Example Topics
Predicting the tissue of origin in cancer based on gene expression levels
Predicting which individuals have COVID-19 based on chest X-ray images
Predicting disease-associated regions of DNA using epigenetic information (Epigenetic information refers to chemical signatures on the DNA that do not alter the DNA sequence but affect how genes are expressed.)
Submission to
Journals
Competitions

Workshop Details

Capacity
8 students
Workshop Fee
US$ 2,850
Format
Zoom
Duration
8 classes in 8 weeks
Fall 2024
Section A
Meeting Times
7:00 PM-8:15 PM ET
Date
Oct 11 - Nov 29 (Fri)
Section B
Meeting Times
8:00 AM - 9:15 AM ET
Date
Oct 13 - Dec 1 (Sun)
Section C
Meeting Times
Date
Section D
Meeting Times
Date
The dates of the last 2 classes may change based on everyone's exam schedules.
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Interested in course, but timing doesn't work?
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Scholarships Available

AlgoEd offers scholarships for this course to ensure educational access for students.

Apply Here
Prerequisites
Knowledge of Python programming.
Homework Load

Approximately 1 hour per week.

Description

Machine learning (ML) is a subfield of artificial intelligence (AI) in which machines learn complex patterns from existing data and perform a task without being explicitly programmed to do so. Today, ML is everywhere. Google’s search algorithms, Netflix’s recommendation services, and many cars’ autopilot systems are prime examples of ML in action. ML is highly used in biology, too, where it can predict the occurrence of disease and advance precision medicine. But ML can still seem like a daunting and scary field to enter, especially for the inexperienced. In this 8-week course, we will translate ML concepts and algorithms into plain English, learning the basics of how ML works and how it is able to make such reliable predictions. Using the Python programming language, students will have a chance to implement an ML algorithm on a real biological dataset to differentiate cancers based on their gene expression profile. This course will focus on using ML in a biological context.

After this course, students who are interested in this area can pursue Mentored Advanced Research (MAP) on topics such as:

  1. Predicting the tissue of origin in cancer based on gene expression levels
  2. Predicting which individuals have COVID-19 based on chest X-ray images
  3. Predicting disease-associated regions of DNA using epigenetic information (Epigenetic information refers to chemical signatures on the DNA that do not alter the DNA sequence but affect how genes are expressed.)
Join Now