TY - JOUR A1 - Mullowney, Michael W. A1 - Duncan, Katherine R. A1 - Elsayed, Somayah S. A1 - Garg, Neha A1 - van der Hooft, Justin J. J. A1 - Martin, Nathaniel I. A1 - Meijer, David A1 - Terlouw, Barbara R. A1 - Biermann, Friederike A1 - Blin, Kai A1 - Durairaj, Janani A1 - Gorostiola González, Marina A1 - Helfrich, Eric J. N. A1 - Huber, Florian A1 - Leopold-Messer, Stefan A1 - Rajan, Kohulan A1 - de Rond, Tristan A1 - van Santen, Jeffrey A. A1 - Sorokina, Maria A1 - Balunas, Marcy J. A1 - Beniddir, Mehdi A. A1 - van Bergeijk, Doris A. A1 - Carroll, Laura M. A1 - Clark, Chase M. A1 - Clevert, Djork-Arné A1 - Dejong, Chris A. A1 - Du, Chao A1 - Ferrinho, Scarlet A1 - Grisoni, Francesca A1 - Hofstetter, Albert A1 - Jespers, Willem A1 - Kalinina, Olga V. A1 - Kautsar, Satria A. A1 - Kim, Hyunwoo A1 - Leao, Tiago F. A1 - Masschelein, Joleen A1 - Rees, Evan R. A1 - Reher, Raphael A1 - Reker, Daniel A1 - Schwaller, Philippe A1 - Segler, Marwin A1 - Skinnider, Michael A. A1 - Walker, Allison S. A1 - Willighagen, Egon L. A1 - Zdrazil, Barbara A1 - Ziemert, Nadine A1 - Goss, Rebecca J. M. A1 - Guyomard, Pierre A1 - Volkamer, Andrea A1 - Gerwick, William H. A1 - Kim, Hyun Uk A1 - Müller, Rolf A1 - van Wezel, Gilles P. A1 - van Westen, Gerard J. P. A1 - Hirsch, Anna K. H. A1 - Linington, Roger G. A1 - Robinson, Serina L. A1 - Medema, Marnix H. T1 - Artificial intelligence for natural product drug discovery JF - Nature Reviews Drug Discovery KW - Maschinelles Lernen KW - Arzneimittelforschung KW - Deep learning KW - Omics-Technologie Y1 - 2023 U6 - https://doi.org/10.1038/s41573-023-00774-7 SN - 1474-1776 VL - 22 IS - 11 SP - 895 EP - 916 PB - Springer Nature ER - TY - JOUR A1 - Haberfehlner, Helga A1 - van de Ven, Shankara S. A1 - van der Burg, Sven A. A1 - Huber, Florian A1 - Georgievska, Sonja A1 - Aleo, Ignazio A1 - Harlaar, Jaap A1 - Bonouvrié, Laura A. A1 - van der Krogt, Marjolein M. A1 - Buizer, Annemieke I. T1 - Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning JF - Frontiers in Robotics and AI Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-40592 SN - 2296-9144 VL - 10 PB - Frontiers ER - TY - JOUR A1 - Bittremieux, Wout A1 - Levitsky, Lev A1 - Pilz, Matteo A1 - Sachsenberg, Timo A1 - Huber, Florian A1 - Wang, Mingxun A1 - Dorrestein, Pieter C. T1 - Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils JF - Journal of proteome research KW - proteomics KW - mass spectrometry KW - metabolomics KW - Python KW - open source Y1 - 2023 U6 - https://doi.org/10.1021/acs.jproteome.2c00632 SN - 1535-3893 N1 - Eingereichte Version ist online verfügbar (Open Access Grün) unter: https://www.biorxiv.org/content/10.1101/2022.10.04.510894v1 VL - 22 IS - 2 SP - 625 EP - 631 PB - American Chemical Society (ACS) ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Louwen, Joris J. R. A1 - Chekmeneva, Elena A1 - Camuzeaux, Stephane A1 - Vermeir, Femke J. A1 - Jansen, Robert S. A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - MS2Query: reliable and scalable MS2 mass spectra-based analogue search JF - Nature Communications N2 - Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology. KW - Metabolomik KW - Massenspektrometrie KW - Maschinelles Lernen KW - Reproduzierbarkeit KW - Metabolomics KW - Mass Spectrometry KW - Machine Learning KW - Complex Mixtures Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46105 SN - 2041-1723 VL - 14 PB - Springer ER -