Using AI to predict drug targets from scRNA-seq data

Saturday, March 1, 10:00AM - 1:00 PM

Sunday, March 2, 10:00AM - 1:00 PM

Drug discovery is a 25 billion dollar industry. With the advent of AI and big data, this number is projected to grow exponentially in 2025. As scientists endeavor to develop personalized patient therapeutics, the role of AI will become indispensable, making computational skills integral to any biologist.

In this workshop, we’ll learn how to build and leverage AI algorithms to predict drug targets as potential therapeutics for Environmental Enteropathy.

In particular, we’ll cover:

• Ingesting and processing a single-cell RNA sequencing disease dataset
• Building a supervised algorithm to predict gene targets
• Developing a reproducible model pipeline for deeper analysis

To process our single cell data, we will rely on commonly used libraries like scanpy and pandas. This will allow us to do quality control (QC) on the data, identify differentially expressed genes, and scale it appropriately for machine learning. To build, train, test and evaluate our AI model, we will use sklearn. This course assumes beginner experience with Python programming and fundamental Biology knowledge.

We will be using Google Colab Notebook for this class - because we are handling big datasets it is recommended that participants purchase the pro version of Google Colab for $10 under the "Pay as you go".

Fact-sheet can be found here.

COVID-19 Safety Notice:

If you are feeling unwell, suspect that you have been exposed to COVID-19 or have tested positive in the past 7 days, please do not attend and let us know ASAP ([email protected]). If you cancel after our 7-day policy, we cannot refund your ticket, but we can exchange and offer credits toward future classes. If you have signed up for Biohacker Boot Camp, we will automatically transfer your registration to the next month’s dates unless you tell us otherwise.

Aneesa Valentine (she/her) is currently a Genomics Solutions Architect at a software company called TileDB. Being formally trained as a scientist, she has a longstanding career in academic research spanning Microbiology, Immunology and most recently, Computational Genomics. After a brief stint as a PhD student, Aneesa mastered out of her doctoral program to pursue a career in Data Science & AI. She holds an MS in Biomedical Science, and has since held Data Scientist titles before pivoting into the world of Life Sciences software. 

She considers herself a translator and finds joy in enabling and empowering the next generation of scientists. She’s given talks to students and professionals alike: endeavoring to make Computational Biology and Data Science accessible to all.

Host or Publisher Genspace NYC