TY - CHAP A1 - Adamatzky, Andrew A1 - Huber, Florian A1 - Schnauß, Jörg ED - Adamatzky, Andrew T1 - Computing on Actin Bundles Network T2 - Actin Computation: Unlocking the Potential of Actin Filaments for Revolutionary Computing System KW - Actin-Filament KW - Informationsverarbeitung KW - Bioinformatik Y1 - 2024 SN - 9789811285066 U6 - https://doi.org/10.1142/9789811285073_0013 SN - 2737-520X VL - Wspc Book Series in Unconventional Computing, Vol. 3 SP - 245 EP - 261 PB - WORLD SCIENTIFIC ER - TY - CHAP A1 - Siccardi, Stefano A1 - Adamatzky, Andrew A1 - Tuszyński, Jack A1 - Huber, Florian A1 - Schnauß, Jörg ED - Adamatzky, Andrew T1 - Actin Networks Voltage Circuits T2 - Actin Computation: Unlocking the Potential of Actin Filaments for Revolutionary Computing Systems KW - Actin-Filament KW - Informationsverarbeitung KW - Bioinformatik Y1 - 2024 SN - 9789811285066 U6 - https://doi.org/10.1142/9789811285073_0006 SN - 2737-520X VL - Wspc Book Series in Unconventional Computing, Vol. 3 SP - 123 EP - 143 PB - WORLD SCIENTIFIC ER - TY - CHAP A1 - Adamatzky, Andrew A1 - Huber, Florian A1 - Schnauß, Jörg ED - Adamatzky, Andrew T1 - Actin Droplet Machine T2 - Actin Computation: Unlocking the Potential of Actin Filaments for Revolutionary Computing Systems Y1 - 2024 SN - 9789811285066 U6 - https://doi.org/10.1142/9789811285073_0014 SN - 2737-520X VL - Wspc Book Series in Unconventional Computing, Vol. 3 SP - 263 EP - 284 PB - WORLD SCIENTIFIC ER - TY - BOOK A1 - Huber, Florian T1 - Hands-on Introduction to Data Science with Python N2 - In today’s world, data is generated at an unprecedented pace, and our ability to harness it is changing the way we live, work, and even think. Data science, the interdisciplinary field that blends statistics, computer science, and domain-specific knowledge, empowers us to extract insights from this vast ocean of data. As data science becomes increasingly essential across various industries and sectors, there is a growing need for skilled professionals who can make sense of data and transform it into actionable information. This book is designed to give you a very broad and at the same time a very practical hands-on tour through the full spectrum of data science approaches KW - Data Science KW - Python (Programmiersprache) KW - Lehrmittel Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-49548 N1 - The DOI represents all versions, and will always resolve to the latest one. The online version of this book can be found here: https://florian-huber.github.io/data_science_course/book/intro.html All materials and source code to render the book can be found on GitHub: https://github.com/florian-huber/data_science_course PB - Zenodo ET - v0.21 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 - Schmid, Robin A1 - Huber, Florian A1 - van der Hooft, Justin J. J. A1 - Wang, Mingxun A1 - Dorrestein, Pieter C. T1 - Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectrum Alignment For Discovery of Structurally Related Molecules JF - Journal of the American Society for Mass Spectrometry KW - Modification KW - Precursors KW - Mass spectrometry KW - Anatomy KW - Peptides and proteins Y1 - 2022 U6 - https://doi.org/10.1021/jasms.2c00153 SN - 1044-0305 N1 - Eingereichte Version ist online verfügbar (Open Access Grün) https://doi.org/10.1101/2022.06.01.494370 unter CC BY 4.0 VL - 33 IS - 9 SP - 1733 EP - 1744 PB - American Chemical Society (ACS) 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 - Mildau, Kevin A1 - Meijer, David A1 - Louwen, Joris J. R. A1 - Bueschl, Christoph A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools JF - Metabolomics N2 - Background Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. Aim of review We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. Key scientific concepts of review This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks. KW - Metabolomik KW - Massenspektrometrie KW - Maschinelles Lernen KW - Benchmarking KW - Mass fragmentation spectra Y1 - 2022 U6 - https://doi.org/10.1007/s11306-022-01963-y SN - 1573-3890 VL - 18 IS - 12 PB - Springer Nature 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 - TY - JOUR A1 - Kamiloğlu, Roza G. A1 - Sun, Rui A1 - Bos, Patrick A1 - Huber, Florian A1 - Attema, Jisk Jakob A1 - Sauter, Disa A. T1 - Tickling induces a unique type of spontaneous laughter JF - Biology Letters N2 - Laughing is ubiquitous in human life, yet what causes it and how it sounds is highly variable. Considering this diversity, we sought to test whether there are fundamentally different kinds of laughter. Here, we sampled spontaneous laughs (n = 887) from a wide range of everyday situations (e.g. comedic performances and playful pranks). Machine learning analyses showed that laughs produced during tickling are acoustically distinct from laughs triggered by other kinds of events (verbal jokes, watching something funny or witnessing someone else’s misfortune). In a listening experiment (n = 201), participants could accurately identify tickling-induced laughter, validating that such laughter is not only acoustically but also perceptually distinct. A second listening study (n = 210) combined with acoustic analyses indicates that tickling-induced laughter involves less vocal control than laughter produced in other contexts. Together, our results reveal a unique acoustic and perceptual profile of laughter induced by tickling, an evolutionarily ancient play behaviour, distinguishing it clearly from laughter caused by other triggers. This study showcases the power of machine learning in uncovering patterns within complex behavioural phenomena, providing a window into the evolutionary significance of ticking-induced laughter. KW - Lachen KW - Kitzel KW - Akustik KW - Maschinelles Lernen KW - Spontaneität KW - Evolutionsbiologie KW - Verhalten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-47446 SN - 1744-957X VL - 20 IS - 11 PB - The Royal Society ER -