TY - JOUR A1 - Adamatzky, Andrew A1 - Schnauß, Jörg A1 - Huber, Florian T1 - Actin droplet machine JF - Royal Society Open Science N2 - The actin droplet machine is a computer model of a three-dimensional network of actin bundles developed in a droplet of a physiological solution, which implements mappings of sets of binary strings. The actin bundle network is conductive to travelling excitations, i.e. impulses. The machine is interfaced with an arbitrary selected set of k electrodes through which stimuli, binary strings of length k represented by impulses generated on the electrodes, are applied and responses are recorded. The responses are recorded in a form of impulses and then converted to binary strings. The machine's state is a binary string of length k: if there is an impulse recorded on the ith electrode, there is a '1' in the ith position of the string, and '0' otherwise. We present a design of the machine and analyse its state transition graphs. We envisage that actin droplet machines could form an elementary processor of future massive parallel computers made from biopolymers. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34675 UR - http://www.ncbi.nlm.nih.gov/pubmed/31903204 SN - 2054-5703 N1 - A.A. was partially supported by EPSRC grant no. EP/P016677/1. The authors declare that they have no competing interests. VL - 6 IS - 12 PB - Royal Soc. Publ. ER - TY - JOUR A1 - Huber, Florian A1 - Ridder, Lars A1 - Verhoeven, Stefan A1 - Spaaks, Jurriaan H. A1 - Diblen, Faruk A1 - Rogers, Simon A1 - van der Hooft, Justin J. J. T1 - Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships JF - PLOS Computational Biology N2 - Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm-Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34687 UR - http://www.ncbi.nlm.nih.gov/pubmed/33591968 SN - 1553-7358 N1 - Data Availability Statement: The pre-processed, cleaned dataset with all positive ionization mode spectra can be downloaded from https://doi.org/10.5281/zenodo.3978118, the original raw data can be accessed from https://doi.org/10.5281/zenodo.3979010. Funding: J.J.J.v.d.H. acknowledges funding from an ASDI eScience grant, ASDI.2017.030, from the Netherlands eScience Center—NLeSC, www.esciencecenter.nl, The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. VL - 17 IS - 2 PB - Cold Spring Harbor Laboratory ER - 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 - CHAP A1 - Gaudry, Arnaud A1 - Huber, Florian A1 - Flückiger, Julien A1 - Quirós, L A1 - Rutz, Adriano A1 - Kaiser, M A1 - Grondin, A A1 - Marcourt, Laurence A1 - Ferreira Queiroz, E A1 - Wolfender, Jean-Luc A1 - Allard, Pierre-Marie T1 - Short Lecture “Mass spectrometry-based sample vectorization for exploration of large chemodiverse datasets and efficient identification of new antiparasitic compounds” T2 - Planta Medica KW - Massenspektrometrie KW - Naturstoffchemie KW - Antiparasitäres Mittel KW - Arzneimittelforschung Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1758983 SN - 1439-0221 VL - 88 IS - 15 PB - Thieme ER - 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 - TY - JOUR A1 - Mildau, Kevin A1 - Ehlers, Henry A1 - Meisenburg, Mara A1 - Del Pup, Elena A1 - Koetsier, Robert A. A1 - Torres Ortega, Laura Rosina A1 - de Jonge, Niek F. A1 - Singh, Kumar Saurabh A1 - Ferreira, Dora A1 - Othibeng, Kgalaletso A1 - Tugizimana, Fidele A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - Effective data visualization strategies in untargeted metabolomics JF - Natural Product Reports N2 - LC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results. KW - Metabolomik KW - Visualisierung Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-47596 SN - 0265-0568 PB - Royal Society of Chemistry ER - TY - INPR A1 - de Jonge, Niek F. A1 - Joas, David A1 - Truong, Lem-Joe A1 - van der Hooft, Justin J.J. A1 - Huber, Florian T1 - Reliable cross-ion mode chemical similarity prediction between MS2 spectra T2 - biorxiv N2 - Mass spectrometry is commonly used to characterize metabolites in untargeted metabolomics. This can be done in positive and negative ionization mode, a choice typically guided by the fraction of metabolites a researcher is interested in. During analysis, mass spectral comparisons are widely used to enable annotation through reference libraries and to facilitate data organization through networking. However, until now, such comparisons between mass spectra were restricted to mass spectra of the same ionization mode, as the two modes generally result in very distinct fragmentation spectra. To overcome this barrier, here, we have implemented a machine learning model that can predict chemical similarity between spectra of different ionization modes. Hence, our new MS2DeepScore 2.0 model facilitates the seamless integration of positive and negative ionization mode mass spectra into one analysis pipeline. This creates entirely new options for data exploration, such as mass spectral library searching of negative ion mode spectra in positive ion mode libraries or cross-ionization mode molecular networking. Furthermore, to improve the reliability of predictions and better cope with unseen data, we have implemented a method to estimate the quality of prediction. This will help to avoid false predictions on spectra with low information content or spectra that substantially differ from the training data. We anticipate that the MS2DeepScore 2.0 model will extend our current capabilities in organizing and annotating untargeted metabolomics profiles. KW - Massenspektrometrie KW - Metabolomik KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/10.1101/2024.03.25.586580 PB - Cold Spring Harbor Laboratory ER - TY - JOUR A1 - Gaudry, Arnaud A1 - Huber, Florian A1 - Nothias, Louis-Félix A1 - Cretton, Sylvian A1 - Kaiser, Marcel A1 - Wolfender, Jean-Luc A1 - Allard, Pierre-Marie T1 - MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets JF - Frontiers in Bioinformatics N2 - In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis. KW - Computational chemistry KW - Massenspektrometrie KW - Metabolomik KW - Naturstoffchemie Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54660 SN - 2673-7647 VL - 2 PB - Frontiers ER - TY - JOUR A1 - Szwarc, Sarah A1 - Rutz, Adriano A1 - Lee, Kyungha A1 - Mejri, Yassine A1 - Bonnet, Olivier A1 - Hazni, Hazrina A1 - Jagora, Adrien A1 - Mbeng Obame, Rany B. A1 - Noh, Jin Kyoung A1 - Otogo N’Nang, Elvis A1 - Alaribe, Stephenie C. A1 - Awang, Khalijah A1 - Bernadat, Guillaume A1 - Choi, Young Hae A1 - Courdavault, Vincent A1 - Frederich, Michel A1 - Gaslonde, Thomas A1 - Huber, Florian A1 - Kam, Toh-Seok A1 - Low, Yun Yee A1 - Poupon, Erwan A1 - van der Hooft, Justin J. J. A1 - Kang, Kyo Bin A1 - Le Pogam, Pierre A1 - Beniddir, Mehdi A. T1 - Translating community-wide spectral library into actionable chemical knowledge: a proof of concept with monoterpene indole alkaloids JF - Journal of Cheminformatics N2 - With over 3000 representatives, the monoterpene indole alkaloids (MIAs) class is among the most diverse families of plant natural products. The MS/MS spectral space exploration of these complex compounds using chemoinformatic and computational mass spectrometry tools offers a valuable opportunity to extract and share chemical insights from this emblematic family of natural products (NPs). In this work, we first present a substantially updated version of the MIADB, a database now containing 422 MS/MS spectra of MIAs that has been uploaded to the GNPS library versus 172 initial entries. We then introduce an innovative workflow that leverages hundreds of fragmentation spectra to support the FAIRification, extraction and dissemination of chemical knowledge. This workflow aims at the extraction of spectral patterns matching finely defined MIA skeletons. These extracted signatures can then be queried against complex biological extract datasets using MassQL. By applying this strategy to an LC-MS/MS dataset of 75 plant extracts, our results demonstrated the efficiency of this approach in identifying the diversity of MIA skeletons present in the analyzed samples. Additionally, our work enabled the digitization of structural data for diverse MIA skeletons by converting them into machine-readable formats and thereby enhancing their dissemination for the scientific community. Scientific contribution A comprehensive investigation of the monoterpene indole alkaloid chemical space, aiming to highlight skeleton-dependent fragmentation similarity trends and to generate valuable spectrometric signatures that could be used as queries. KW - Computational chemistry KW - Massenspektrometrie KW - Naturstoffchemie Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54607 SN - 1758-2946 VL - 17 IS - 1 PB - Springer Nature ER - TY - INPR A1 - Huber, Florian A1 - Pollmann, Julian T1 - Count your bits: more subtle similarity measures using larger radius count vectors N2 - Quantifying molecular similarity is a cornerstone of cheminformatics, underpinning applications from virtual screening to chemical space visualization. A wide range of molecular fingerprints and similarity metrics, most notably Tanimoto scores, are employed, but their effectiveness is highly context-dependent. In this study, we systematically evaluate several 2D fingerprint types, including circular, path-based, and distance-encoded variants, using both binary and count representations. We highlight the consequences of fingerprint choice, vector folding, and similarity metric selection, revealing critical issues such as fingerprint duplication, mass dependent score biases, and high bit collision rates. Sparse and count-based fingerprints consistently outperform fixed-size binary vectors in preserving structural distinctions. Furthermore, we introduce percentile-based normalization, propose inverse-document-frequency (IDF) weighting, and benchmark all methods against graph-based MCES similarities. Our results offer practical guidance for selecting molecular similarity measures, emphasizing the need for conscious, task-aware fingerprinting choices in large-scale chemical analyses. KW - Computational chemistry KW - Metabolomik KW - Naturstoffchemie Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54632 PB - bioRXiv ER - TY - JOUR A1 - Hunke, Til A1 - Huber, Florian A1 - Steffens, Jochen T1 - The Evolution of Song Lyrics: An NLP-Based Analysis of Popular Music in Germany from 1954 to 2022 JF - Music & Science N2 - Music is an indispensable cultural product, reflecting changes in social, psychological, and cultural contexts. This study analyzes the historical evolution of topics and conveyed affect in popular music lyrics in Germany from 1954 to 2022, using LDA-based topic modeling and transformer-based sentiment analysis. These results show that Love & Relationships is the most referenced topic, with Dreams & Longings prominent until the mid-1960s and Society & Status rising from 2017. The sentiment analysis reveals a significant decline in positive sentiment since the mid-1960s, accompanied by increases in negative, ambiguous, and neutral sentiments. These trends may reflect broader societal changes, including shifts in cultural values, rising individualism, and increasing mental health issues. The study highlights the evolving nature of popular music and its reflection of social dynamics. KW - Popmusik KW - Maschinelles Lernen KW - Automatische Sprachanalyse Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-52618 SN - 2059-2043 VL - 8 PB - Sage Publications ER - TY - JOUR A1 - Kok, Maurits A1 - Huber, Florian A1 - Kalisch, Svenja-Marei A1 - Dogterom, Marileen T1 - EB3-informed dynamics of the microtubule stabilizing cap during stalled growth JF - Biophysical Journal N2 - Microtubule stability is known to be governed by a stabilizing GTP/GDP-Pi cap, but the exact relation between growth velocity, GTP hydrolysis, and catastrophes remains unclear. We investigate the dynamics of the stabilizing cap through in vitro reconstitution of microtubule dynamics in contact with microfabricated barriers, using the plus-end binding protein GFP-EB3 as a marker for the nucleotide state of the tip. The interaction of growing microtubules with steric objects is known to slow down microtubule growth and accelerate catastrophes. We show that the lifetime distributions of stalled microtubules, as well as the corresponding lifetime distributions of freely growing microtubules, can be fully described with a simple phenomenological 1D model based on noisy microtubule growth and a single EB3-dependent hydrolysis rate. This same model is furthermore capable of explaining both the previously reported mild catastrophe dependence on microtubule growth rates and the catastrophe statistics during tubulin washout experiments. KW - Mikrotubulus Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-53164 SN - 1542-0086 VL - 124 IS - 2 SP - 227 EP - 244 PB - Elsevier ER - TY - INPR A1 - Bushuiev, Roman A1 - Bushuiev, Anton A1 - de Jonge, Niek F. A1 - Young, Adamo A1 - Kretschmer, Fleming A1 - Samusevich, Raman A1 - Heirman, Janne A1 - Wang, Fei A1 - Zhang, Luke A1 - Dührkop, Kai A1 - Ludwig, Marcus A1 - Haupt, Nils A. A1 - Kalia, Apurva A1 - Brungs, Corinna A1 - Schmid, Robin A1 - Greiner, Russell A1 - Wang, Bo A1 - Wishart, David S. A1 - Liu, Li-Ping A1 - Rousu, Juho A1 - Bittremieux, Wout A1 - Röst, Hannes A1 - Mak, Tytus D. A1 - Hassoun, Soha A1 - Huber, Florian A1 - van der Hooft, Justin J.J. A1 - Stravs, Michael A. A1 - Böcker, Sebastian A1 - Sivic, Josef A1 - Pluskal, Tomáš T1 - MassSpecGym: A benchmark for the discovery and identification of molecules T2 - arXiv N2 - The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at this https URL [https://github.com/pluskal-lab/MassSpecGym]. KW - Maschinelles Lernen KW - Benchmark KW - Computational chemistry KW - Massenspektrometrie KW - Molekülstruktur Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2410.23326 N1 - Open Review: https://web.archive.org/web/20250521010648/https://openreview.net/forum?id=AAo8zAShX3#discussion PB - arXiv ET - v3 ER - TY - CHAP A1 - Steffens, Jochen A1 - Joschko, Marcel A1 - Giang, Hien A1 - Huber, Florian T1 - Using machine learning to identify acoustic fingerprints of concert halls in classical audio recordings [Abstract] T2 - DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen KW - Datenanalyse KW - Maschinelles Lernen KW - Raumakustik KW - Konzertsaal Y1 - 2025 UR - https://pub.dega-akustik.de/DAS-DAGA_2025/konferenz-1559.html?article=348 PB - Deutsche Gesellschaft für Akustik e.V. CY - Berlin ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Hecht, Helge A1 - Strobel, Michael A1 - Wang, Mingxun A1 - van der Hooft, Justin J. J. A1 - Huber, Florian T1 - Reproducible MS/MS library cleaning pipeline in matchms JF - Journal of Cheminformatics N2 - Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries. KW - Massenspektrometrie KW - Metabolomik KW - Metadaten KW - Python (Programmiersprache) KW - Library cleaning KW - HSD Publikationsfonds KW - DFG Publikationskosten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46491 SN - 1758-2946 VL - 16 IS - 1 PB - Springer Nature ER -