Introduction to JADBio: Automated Machine Learning for the Biologist
- When – Tuesday, November 17th, 12-1:30 PM
- Who – Ioannis Tsamardinos, PhD, CEO & Co-Founder, JADBio
- How – click here to register & get Zoom info
Professor Ioannis Tsamardinos will present the science and practice of JADBio (or Just Add Data), an automated machine learning tool, specifically designed for biomedical data, such as multi-omics studies.
JADBio‘s easy-to-use interface allows biologists, bioinformaticians, clinicians, and non-expert analysts to perform sophisticated analyses with the click of a button. It is fully automatic drastically boosting productivity, even for expert analysts. It tries thousands of combinations of algorithms and tuning parameters to find the optimal model. Novel statistical methods avoid overfitting and overestimation of performance even for low sample sizes. It performs feature selection (biosignature identification) by removing irrelevant, but also redundant features (markers) for prediction. It has been validated on hundreds of public datasets, producing novel scientific results.
The webinar will present the functionalities and capabilities of the automated machine learning tool, demonstrated by several case studies in biomedicine. It will also discuss the science behind it and its novel algorithms. The webinar participants will also able to register with their Pitt emails and test, free of cost, the JADBio platform for an extended 3-month period.
Who should attend
biologists, translational researchers, clinical researchers, bioinformaticians, data analysts, data scientists, machine learning experts, statistical analysis experts
About the speaker
Ioannis Tsamardinos, Ph.D., is a Professor at the Computer Science Department of University of Crete and co-founder of Gnosis Data Analysis PC, a University start-up. He obtained his Ph.D. from the Intelligent Systems Program at the University of Pittsburgh in 2001. He then worked as Assistant Professor at the Department of Biomedical Informatics at Vanderbilt University until 2006 when he returned to Greece. Prof. Tsamardinos’ main research directions include machine learning, bioinformatics, and artificial intelligence. More specifically his work emphasizes feature selection, causal discovery, and automation of machine learning.