The NanoFANS Forum will focus on "Trends in Machine Learning for Biology." Machine learning (ML) in the field of biology has multiple applications, ranging from natural language processing to healthcare. ML, through deep learning algorithms, extracts meaningful information from huge datasets such as genomes or a group of images and builds a model based on the extracted features. The model is then used to perform analysis on other biological datasets. ML has been applied to various biological domains, such as genomics, proteomics, phylogenetics, systems biology, text mining, microarray data analysis, and other areas including primer design, image analysis, and experimental data management.

IEN’s 27th NanoFANS forum will explore the emerging trends in the area of machine learning for biology. Speakers will include Regents` Professor David Sherrill, Regents` Professor Jeffrey Skolnick, Professor Hannah Choi, and Professor King Jordan from the Georgia Institute of Technology. They will discuss their current research activities in the area of machine learning for biology.

Please register by June 19, 2023, to reserve your seat. Lunch will be provided!

After the talks, an optional tour of the state-of-the-art IEN cleanrooms and labs will be available for interested participants. Please let us know if you are interested in taking a tour.

Schedule

11:00 a.m. - 11:10 a.m. - Welcome

11:10 a.m. - 11:50 a.m. - “Machine learning for protein-ligand interactions”
David Sherrill, Regents' Professor of Chemistry and Biochemistry at Georgia Tech
 

11:50 a.m. - 12:30 p.m. - “Deep learning approaches to predict protein com-plexes”
Jeffrey Skolnick, Regents' Professor of Biological Sciences at Georgia Tech

12:30 p.m. - 1:10 p.m. - Lunch Break
1:10 p.m. - 1:50 p.m. - “Neural computations in data-driven networks”

Hannah Choi, Professor of Mathematics at Georgia Tech
1:50 p.m. - 2:30 p.m. - "Genomic diversity for health equity"

King Jordan, Professor of Biological Sciences at Georgia Tech
2:30 - 3:00 p.m. - IEN Cleanroom/MCF Tour (Optional)