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Co-clustering of biological networks and gene expression data

Bioinformatics (Oxford, England) 2002年第SUPPL_1期18 Suppl 1卷 S145-54页

作者： Daniel Hanisch Alexander Zien Ralf Zimmer Thomas Lengauer Institute for Algorithms and Scientific Computing (SCAI) Fraunhofer Gesellschaft Schloss Birlinghoven Sankt Augustin 53754 Germany. Daniel.Hanisch@scai.fhg.de

MOTIVATION:Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS:We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

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Improving fold recognition of protein threading by experimental distance constraints

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In silico biology 2002年第3期2卷 325-37页

作者： Mario Albrecht Daniel Hanisch Ralf Zimmer Thomas Lengauer Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven D-53 754 Sankt Augustin Germany. mario.albrecht@scai.fhg.de

We present a comprehensive analysis of methods for improving the fold recognition rate of the threading approach to protein structure prediction by the utilization of few additional distance constraints. The distance constraints between protein residues may be obtained by experiments such as mass spectrometry or NMR spectroscopy. We applied a post-filtering step with new scoring functions incorporating measures of constraint satisfaction to ranking lists of 123D threading alignments. The detailed analysis of the results on a small representative benchmark set show that the fold recognition rate can be improved significantly by up to 30% from about 54%-65% to 77%-84%, approaching the maximal attainable performance of 90% estimated by structural superposition alignments. This gain in performance adds about 10% to the recognition rate already achieved in our previous study with cross-link constraints only. Additional recent results on a larger benchmark set involving a confidence function for threading predictions also indicate notable improvements by our combined approach, which should be particularly valuable for rapid structure determination and validation of protein models.

关键词： protein threading fold recognition structure prediction experimentaldata distance constraints cross-linking reagents mass spectrometry NOE restraints NMR

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DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

DARE: A Reflective Platform Designed to Enable Agile Data-Dr...

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IEEE International Conference on e-Science and Grid computing

作者： Iraklis Klampanos Athanasios Davvetas André Gemünd Malcolm Atkinson Antonios Koukourikos Rosa Filgueira Amrey Krause Alessandro Spinuso Angelos Charalambidis Federica Magnoni Emanuele Casarotti Christian Pagé Mike Lindner Andreas Ikonomopoulos Vangelis Karkaletsis Institute of Informatics and Telecommunications NCSR "Demokritos" Greece Fraunhofer Institute for Algorithms and Scientific Computing School of Informatics The University of Edinburgh UK NCSR "Demokritos" EPCC The University of Edinburgh UK Koninklijk Nederlands Meteorologisch Instituut (KNMI) The Netherlands Istituto Nazionale Geofisica e Vulcanologia (INGV) Italy Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique Karlsruher Institut f??r Technologie (KIT) Germany Institute of Nuclear & Radiological Sciences and Technology Energy & Safety NCSR "Demokritos" Greece

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.

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On the Trade-Off between Fidelity, Utility and Privacy of Synthetic Patient Data

SSRN

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SSRN 2024年

作者： Adams, Tim Birkenbihl, Colin Otte, Karen Ng, Hwei Geok Rieling, Jonas Adrian Näher, Anatol-Fiete Sax, Ulrich Prasser, Fabian Fröhlich, Holger Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany Berlin Germany Department of Medical Informatics University Medical Center Goettingen Goettingen Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Berlin Germany Bonn-Aachen International Center for IT Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Neurology Massachusetts General Hospital Harvard Medical School Boston United States

The advancement of medical research and healthcare is increasingly dependent on the analysis of patient-level data, but privacy concerns and legal constraints often hinder data sharing. Synthetic data mimicking real patient data offers a widely discussed potential solution. According to the literature, synthetic data may, however, not fully guarantee patient privacy and can vary greatly in terms of fidelity and utility. In this study, we aim to systematically investigate the trade-off between privacy, fidelity and utility of synthetic patient data. We assess synthetic data fidelity in terms of statistical similarity to real data, and utility via the performance of machine learning models trained on synthetic and tested on real data. Regarding data privacy we focus on membership inference via shadow model attacks as well as singling out and attribute inference risks. In this regard, we also consider differential privacy (DP) as a possible mechanism to probabilistically guarantee a certain level of data privacy, and we compare against classical anonymization techniques. We evaluate the fidelity, utility and privacy of synthetic data generated by five different models for three distinctive patient-level datasets. Our results show that our implementations of DP have a strongly detrimental effect on the fidelity of synthetic data, specifically its correlation structure, and therefore emphasize the need to improve techniques that effectively balance privacy, fidelity and utility in synthetic patient data generation. © 2024, The Authors. All rights reserved.

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From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine

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EPMA Journal 2014年第1期5卷 1-1页

作者： Erfan Younesi Martin Hofmann-Apitius Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven Sankt Augustin Germany Rheinische Friedrich-Wilhelms-Universität Bonn Bonn-Aachen International Center for IT Bonn Germany

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SYNTHETIC DATA GENERATION FOR A LONGITUDINAL COHORT STUDY - EVALUATION, METHOD EXTENSION AND REPRODUCTION OF PUBLISHED DATA ANALYSIS RESULTS

arXiv

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arXiv 2023年

作者： Kühnel, Lisa Schneider, Julian Perrar, Ines Adams, Tim Prasser, Fabian Nöthlings, Ute Fröhlich, Holger Fluck, Juliane Knowledge Management Zb Med Information Centre for Life Sciences Cologne50931 Germany Faculty of Technology Bielefeld University Bielefeld33615 Germany Institute of Nutritional and Food Sciences - Nutritional Epidemiology University of Bonn Bonn53115 Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Scai Sankt Augustin53757 Germany Medical Informatics Group Berlin Institute of Health Charité - Universitätsmedizin Berlin Berlin10117 Germany Bonn-Aachen International Center for It University of Bonn Friedrich Hirzebruch-Allee 6 Bonn53115 Germany The Agricultural Faculty University of Bonn Bonn53115 Germany

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e. data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case. © 2023, CC BY.

关键词： Large dataset

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Resonant Solitary States in Complex Networks

arXiv

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arXiv 2024年

作者： Niehues, Jakob Yanchuk, Serhiy Berner, Rico Kurths, Jürgen Hellmann, Frank Anvari, Mehrnaz Member of the Leibniz Association P.O. Box 60 12 03 PotsdamD-14412 Germany Humboldt-Universität zu Berlin Department of Physics Newtonstraße 15 Berlin12489 Germany Technische Universität Berlin ER 3-2 Hardenbergstrasse 36a Berlin10623 Germany University College Cork School of Mathematical Sciences Western Road CorkT12 XF62 Ireland Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany

Partially synchronized solitary states occur frequently when a synchronized system of networked oscillators with inertia is perturbed locally. Several asymptotic states of different frequencies can coexist at the same node. Here, we reveal the mechanism behind this multistability: additional solitary frequencies arise from the coupling between network modes and the solitary oscillator’s frequency, leading to significant energy transfer. This can cause the solitary node’s frequency to resonate with a Laplacian eigenvalue. We analyze which network structures enable this resonance and explain longstanding numerical observations. Another solitary state that is known in the literature is characterized by the effective decoupling of the synchronized network and the solitary node at the natural frequency. Our framework unifies the description of solitary states near and far from resonance, allowing to predict the behavior of complex networks from their topology. © 2024, CC BY.

关键词： Synchronization

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A knowledge-based surrogate modeling approach for cup drawing with limited data

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IOP Conference Series: Materials Science and Engineering 2019年第1期651卷

作者： L Morand D Helm R Iza-Teran J Garcke Fraunhofer Institute for Mechanics of Materials IWM Wöhlerstraße 11 Freiburg 79108 Germany Fraunhofer Center for Machine Learning and Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin 53754 Germany Institute for Numerical Simulations University of Bonn Endenicher Allee 19b Bonn 53115 Germany

To predict the quality of a process outcome with given process parameters in real-time, surrogate models are often adopted. A surrogate model is a statistical model that interpolates between data points obtained either by process measurements or deterministic models of the process. However, in manufacturing processes the amount of useful data is often limited, and therefore setting up a sufficiently accurate surrogate model is challenging. The present contribution shows how to handle limited data in a surrogate modeling approach using the example of a cup drawing process. The purpose of the surrogate model is to classify the quality of the drawn cup and to predict its final geometry. These classification and regression tasks are solved via machine learning methods. The training data is sampled on a relatively wide range varying three parameters of a finite element simulation, namely sheet metal thickness, blank holder force, and friction. The geometrical features of the cup are extracted using domain knowledge. Besides this knowledge-based approach, an outlook is given for a data-driven surrogate modeling approach.

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Publisher Correction: ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases

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npj Systems Biology and Applications 2019年第1期5卷 1页

作者： Daniel Domingo-Fernández Charles Tapley Hoyt Carlos Bobis-Álvarez Josep Marín-Llaó Martin Hofmann-Apitius Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Bonn-Aachen International Center for IT Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Faculty of Medicine and Health Sciences University of Oviedo Oviedo Spain Rovira i Virgili University Tarragona Spain

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Evaluating digital device technology in Alzheimer’s disease via artificial intelligence

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Alzheimer's & Dementia 2021年第S6期17卷

作者： Meemansa Sood Robbert Harms Maximilian Bügler Ioannis Tarnanas Neva Coello Holger Fröhlich Bonn-Aachen International Center for IT (b-it) Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Altoida Inc. Houston TX USA Altoida AG Hirschengraben Lucerne Switzerland Global Brain Health Institute Trinity College Dublin Ireland Hellenic Initiative Against Alzheimer's Disease Johns Hopkins Precision Medicine Center Baltimore MD USA Novartis Basel Switzerland

Background Digital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer’s Disease (AD) in a pre-symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR-AD ( *** ) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living. Method An example of a panel of digital technology RADAR-AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re-ran VAMBN. Result Application of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub-item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM’s and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms. Conclusion Our results indicate t

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