AI and data in cancer research
Artificial intelligence is playing an increasingly important role in cancer research. Advancement in acquisition technologies has led to largescale data production of various molecular readouts. Coupled with smart learning algorithms, this brings exciting challenges and opportunities for early detection, diagnostic, treatment selection and monitoring of cancers. Is it possible to identify cancers early in a non-invasive manner? Can cross-cancer analysis offer guidance to treatment of patients whose tumors reside in a different organ and yet sharing driver gene mutations with other tumors that have previously been successfully managed?
In this presentation, I will survey current methods and data in cancer genomics and proteomics with a view towards real-life applications. I will also discuss sustainable software tools and computing infrastructures to maintain the momentum of AI in the field of cancer research.
Dr. Pham Viet Thang – VU University Medical Center (Netherland)
Pham Viet Thang received a BSc degree in computer science from RMIT University, Australia in 1998. After a brief period working as teaching assistant at Vietnam National University in 1999, he joined the Intelligent Systems Lab Amsterdam, the Netherland where he earned a doctoral degree on the topic of machine learning and computer vision. During this period, he devised new methods for Bayesian network classification, support vector classification, boosting algorithms, and sparse representation of images. Since 2006, he has been working first as research scientist and now as assistant professor at the Cancer Center Amsterdam, VU University Medical Center, the Netherlands. His current interest is to advance computer algorithms to explore the vast amount of data in cancer research. A highlight of his work is the development of statistical methods for significance analysis of mass spectrometry-based proteomics data.