Machine-learning Models for Big Data Omics: Challenges, Opportunities and Progress
This is a past event.
Friday, February 26 at 11:00am to 12:00pmVirtual Event
Join us for a talk on "Machine-learning Models for Big Data Omics: Challenges, Opportunities and Progress" with Dr. Fahad Saeed, Associate Professor, School of Computing and Information Sciences, FIU.
Machine learning (ML) has emerged as a discipline that enables assistance to humans in making sense of big data sets from large and complex systems biology experiments. Drop in the cost of producing the data has made these large data sets accessible to researchers for various investigations. Analyzing these complex and big data sets is not trivial, and classical algorithms and models cannot fully explore the full potential that can move closer to personalized medicine. Machine learning algorithms can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of medicine and improve health care. There is an increasing interest in the potential of ML to create predictive models and to identify complex patterns from omics datasets. In this talk, he will discuss the challenges in developing machine-learning models for complex Mass Spectrometry based Proteomics data. The newly developed machine-learning tools are drastically changing the way the proteomics data are analyzed. Further, he will discuss the challenges, and opportunities in developing machine-learning models for big brains, and their role in quantifying, and diagnosing mental disorders. Lastly, he will discuss the opportunities and progress in developing high performance computing solutions for these machine-learning models for efficient, and timely computations.
The seminar will take place on Friday, February 26th, 11:00AM via Zoom meeting. Please email email@example.com for Zoom link.