William Stafford Noble (formerly William Noble Grundy) received the Ph.D. in computer science and cognitive science from UC San Diego in 1998. After a one-year postdoc with David Haussler at UC Santa Cruz, he became an Assistant Professor in the Department of Computer Science at Columbia University. In 2002, he joined the faculty of the Department of Genome Sciences at the University of Washington. His research group develops and applies statistical and machine learning techniques for modeling and understanding biological processes at the molecular level. Noble is the recipient of an NSF CAREER award and is a Sloan Research Fellow and a Fellow of the International Society for Computational Biology.
Machine learning and statistical challenges in protein mass spectrometry
Bioinformatics is driven in large part by technological innovation, such as the advances in DNA sequencing for genomics. The field of proteomics is undergoing a similar, technology-driven transformation, as the field begins to switch from data-dependent acquisition to data-independent acquisition. I will explain this transformation and its implications for our understanding of the dynamic proteome, giving examples of new machine learning and statistical challenges that arise from the resulting big data sets.