Professor Lukas Käll, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH – Royal Institute of Technology, Stockholm, Sweden
Machine learning methods for the interpretation of label-free proteomics data

Host: Laura Elo (laura.elo@utu.fi)

Coffee and cookies will be served at 9:45.

Lukas Käll has a Ph.D. from Karolinska Institutet and completed a post-doc at the University of Washington, USA. His group develops machine-learning methods for facilitating the interpretation of high-throughput experiments. He has developed a wide variety of bioinformatical tools, such as Phobius, MaRaCluster, and Percolator. Lukas Käll is currently a Professor of Applied Systems Biology at KTH – Royal Institute of Technology, Science for Life Laboratory.

Publication list:
https://scholar.google.se/citations?user=aZALISYAAAAJ&hl=en

Publications mentioned in the talk
Semi-supervised learning for peptide identification from shotgun proteomics datasets
L Käll, JD Canterbury, J Weston, WS Noble, MJ MacCoss. Nature methods 4 (11), 923, 2007

MaRaCluster: A fragment rarity metric for clustering fragment spectra in shotgun proteomics.
M The, L Käll. Journal of Proteome Research 15 (3), 713-720, 2016

Integrated identification and quantification error probabilities for shotgun proteomics
M The, L Käll. Molecular & Cellular Proteomics 18 (3), 561-570, 2019

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
M The, L Käll. BioRxiv, 488015, 2018
https://www.biorxiv.org/content/biorxiv/early/2018/12/06/488015.full.pdf