Topic: Artificial Intelligence / Machine Learning

Registration: https://btep.ccr.cancer.gov/classes/ai_three/

Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m933690e1ccdbfd15ae3f75d1cbec3b95

Description: This talk will describe machine learning and deep learning methods to analyze bulk and single-cell RNA sequencing data, as well as deep learning models that integrate epigenetic data to decipher the regulatory networks underlying gene expression.

Presenter: Avantika Lal PhD, Senior Scientist | Deep Learning and Genomics | NVIDIA

Participants will learn how to develop artificial intelligence (AI) applications focused on biomedical imaging datasets. The course will cover the entire AI pipeline from image exploration and labeling to development and deployment of predictive models on images using both machine learning and deep learning approaches. This includes annotating and exploring image datasets, exploring techniques for developing machine and deep learning models, optimizing the parameters of the deep networks, and automatically generating code for edge deployment of AI models.

This is an introductory level class. No installation of MATLAB is necessary.

Register

Registration: https://btep.ccr.cancer.gov/classes/ai_four/

Meeting Link: https://cbiit.webex.com/cbiit/j.php?MTID=m5fa0e43ae167ed5ea3a77fb25d339a82

Description: In this talk, we will highlight two examples for building predictive models from multi modal data. The first example predicts dose response in cell lines based on drug and molecular features. The second example will show to combine pathology whole slide images and molecular features for cancer diagnosis and prognosis.

Presenters: George Zaki, Bioinformatics Manager, Strategic and Data Science Initiatives (SDSI), Frederick National Laboratory for Cancer Research (FNL), Pinyi Lu, Bioinformatics analyst, SDSI, FNL