Google scholar profile

Selected publications

The, M., Käll, L. (2016). “MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics.” J. Proteome Res., 15(3), 713-720.

The, M., MacCoss, M.J., Noble, W.S., Käll, L. (2016) “Fast and accurate protein false discovery rates on large-scale proteomics data sets with Percolator 3.0.” J. Am. Soc. Mass Spectrom., 27(11), 1719-1727.

The, M., Tasnim, A., & Käll, L. (2016). “How to talk about protein‐level false discovery rates in shotgun proteomics.” Proteomics, 16(18), 2461-2469.

The, M., Käll, L. (2018). “Integrated identification and quantification error probabilities for shotgun proteomics.” Molecular & Cellular Proteomics, mcp-RA118.

Other publications

The, M. (2018). “Statistical and machine learning methods to analyze large-scale mass spectrometry data.” Doctoral Thesis, KTH – Royal Institute of Technology, Stockholm, Sweden.

Maboudi Afkham, H., Qiu, X., The, M., Käll, L. (2016). “Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics.” Bioinformatics, 33(4), 508-513

Griss, J., Perez-Riverol, Y., The, M., Käll, L., & Vizcaíno, J. A. (2018). “Response to “Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra”.” J. Proteome Res., 17(5), 1993-1996.

The, M., Edfors, F., Perez-Riverol, Y., Payne, S.H., Hoopmann, M.R., Palmblad, M., Forsström, B. & Käll, L. (2018). “A protein standard that emulates homology for the characterization of protein inference algorithms.” J. Proteome Res., 17(5), 1879-1886.

Lee, J.Y., Choi, H., Colangelo, C.M., Davis, D., Hoopmann, M.R., Käll, L., Lam, H., Payne, S.H., Perez-Riverol, Y., The, M. & Wilson, R. (2018). “ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data.” J. Biomol. Tech., 29(2), 39-45.