Key publications

Fast and accurate database searches with MS-GF+ Percolator. V Granholm, S Kim, JCF Navarro, E Sjölund, RD Smith, L Käll. Journal of proteome research (2014), 13 (2), pp 890–897

Optimized nonlinear gradients for reversed-phase liquid chromatography in shotgun proteomics. L Moruz, P Pichler, T Stranzl, K Mechtler, L Käll. Analytical chemistry (2013) 85 (16), 7777-7785

Recognizing uncertainty increases robustness and reproducibility of mass spectrometry-based protein inferences O Serang, L Moruz, MR Hoopmann, L Käll. Journal of proteome research (2012), 11 (12), 5586-5591

On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics. V Granholm, WS Noble, L Käll. Journal of proteome research (2011), 10 (5), 2671-2678

Training, selection, and robust calibration of retention time models for targeted proteomics. L Moruz, D Tomazela, L Käll. Journal of proteome research( 2010) 9 (10), 5209-5216

Lukas Käll

Research interests

Modern biology is to increasing degree dependent on so called high-throughput techniques, i.e. massively parallel experiments that generate a large set of readouts. Examples of such techniques are shotgun proteomics and massively parallel sequencing. A common challenge for these kinds of experiments is that the interpretation of the outcomes, as the individual measurements are of varying quality. We are aiming at increasing the yield and facilitating the interpretation of high-throughput experiments by using different machine learning methods such as support vector machines and dynamical Bayesian networks.

Group members

Heydar Maboudi Afkham, Postdoc
Matthew The, PhD-student


Last updated: 2023-07-28

Content Responsible: admin(