Event Details

MIST Guest Seminar

21st October at 15:00
Onsite event
in Lauren 2 (MED D1016), Medisiina D

Prof. William Stafford Noble, University of Washington, USA
Machine learning methods for proteomics mass spectrometry data

Host: Balazs Balint (balazs.balint@utu.fi)

Coffee and pulla served at 14:30, first come first serve!

Noble research group develops and applies computational techniques for modeling and understanding biological processes at the molecular level. Their research emphasizes the application of statistical and machine learning techniques, such as hidden Markov models and support vector machines. The team applies these techniques to various types of biological data, including protein and DNA sequences, data from high-throughput genomic assays such as ChIP-seq and Hi-C, and tandem mass spectrometry. Prof. Noble’s research group is currently developing methods for analyzing shotgun proteomics data, for characterizing protein function, structure and interactions, and for understanding the structure and regulatory influence of chromatin.

William Stafford Noble is a Professor in the Department of Genome Sciences and in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He received the Ph.D. in computer science and cognitive science from University of California, San Diego in 1998. Dr. Noble’s research applies statistical and machine learning methods to the analysis of complex biological data sets. He is the author of more than 350 peer reviewed publications and has advised 39 postdoctoral fellows and 31 PhD students. William is the recipient of the International Society for Computational Biology Innovator award, the US Human Proteome Organization Gilbert S. Omenn Computational Proteomics Award, and is a Fellow and former member of the Board of Directors of the ISCB.

Selected publications

Jha A, Hristov B, Wang X, Wang S, Greenleaf WJ, Kundaje A, Aiden EL, Bertero A, Noble WS. Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC. Nat Commun. 2026 Apr 20. in press

Sanders J, Wen B, Rudnick PA, Johnson RS, Wu CC, Riffle M, Oh S, MacCoss MJ, Noble WS. A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data. Nat Methods. 2025 Jul;22(7):1447-1453.

Sanders J, Wen B, Rudnick PA, Johnson RS, Wu CC, Riffle M, Oh S, MacCoss MJ, Noble WS. A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data. Nat Methods. 2025 Jul;22(7):1447-1453.

Bittremieux W, Noble WS. Self-supervised learning from small-molecule mass spectrometry data. Nat Biotechnol. 2026 Apr;44(4):538-539.