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2013 German Conference on bioinformatics, GCB 2013

作者： Fröhlich, Holger Klau, Gunnar W. Bonn-Aachen International Center for IT Algorithmic Bioinformatics University of Bonn Dahlmannstr. 2 53113 Bonn Germany Life Sciences Science Park 123 1098 XG Amsterdam Netherlands

ISBN: (纸本)9783939897590

Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies are applied to obtain edge frequencies. In a more general sense edge frequencies can also result from integrating networks learned on different datasets or via different inference algorithms. Subsequently one typically wants to derive a biological interpretation from the results in terms of a consensus network. We here propose a log odds based edge score on the basis of the expected false positive rate and thus avoid the selection of a subjective edge frequency cutoff. Computing a score optimal consensus network in our new model amounts to solving the maximum weight acyclic subdigraph problem. We use a branch-and-cut algorithm based on integer linear programming for this task. Our empirical studies on simulated and real data demonstrate a consistently improved network reconstruction accuracy compared to two threshold based strategies.

关键词： Reverse engineering

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A Robust Framework for Rapid Deployment of a Virtual Screening Laboratory

A Robust Framework for Rapid Deployment of a Virtual Screeni...

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7th Healthgrid Conference

作者： Shahid, Mohammad Hofmann-Apitius, Martin Waldrich, Oliver Ziegler, Wolfgang Fraunhofer Institute SCAI Department of Bioinformatics Germany Bonn-Aachen International Center for Information Technology Bonn Germany

ISBN: (纸本)9781607500278

A high throughput framework for virtual screening (VS) applications has been designed which enables researchers in m-silico drug discovery to deploy large scale time-consuming virtual screening experiments on High performance Grid computing environments Issues related to enabling virtual screening on grids have been addressed extensively in the past, in this paper we describe a new framework, which supports the rapid deployment of virtual screening services based on UNICORE-6 and Meta Scheduling Service (MSS) We demonstrate, how the deployment of complex virtual screening workflows is facilitated by this framework and we provide some first results of the virtual laboratory testbed established in the course of the EU project PHOSPHORUS The flexibility of the new VS framework allows easy deployment and integration of new resources and applications, making the Grid more accessible to the users

关键词： Virtual High Throughput Screening vHTS VS Framework Grid Computing Meta Scheduling Service UNICORE-6 Workflows SLAs

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Predicting Comorbidities of Epilepsy Patients Using Big Data from Electronic Health Records Combined With Biomedical Knowledge

Predicting Comorbidities of Epilepsy Patients Using Big Data...

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2017 German Conference on bioinformatics, GCB 2017

作者： Gerlach, Thomas Lu, Chao Fröhlich, Holger UCB Biosciences GmbH Monheim Germany University of Bonn Bonn-Aachen International Center for IT Life Sciene DataAnalytics and Algorithmic Bioinformatics Germany UCB Ltd. Raleigh United States

Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities. While general comorbidity prevalence and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting individual comorbidities. To address these needs we used Big Data from electronic health records (100 Million raw observations), which provide a time resolved view on an individual's disease and medication history. A specific contribution of this work is an integration of these data with information from 14 biomedical sources (DisGeNET, TTD, KEGG, Wiki Pathways, DrugBank, SIDER, Gene Ontology, Human Protein Atlas, ...) to capture putative biological effects of observed diseases and applied medications. In consequence we extracted >165,000 features describing the longitudinal patient journey of >10,000 adult epilepsy patients. We used maximum-relevance-minimum-redundancy feature selection in combination with Random Survival Forests (RSF) for predicting the risk of 9 major comorbidities after first epilepsy diagnosis with high cross-validated C-indices of 76-89% and analyzed the influence of medications on the risk to develop specific comorbidities. Altogether we see our work as a first step towards earlier detection and better prevention of common comorbidities of epilepsy patients. Supplementary material: https://***/file/d/0B4OhgVPeWvGTeUNFQVJLai1HRlk/view?usp=sharing, code: https://***/thomasmooon/GCB2017. © 2017 German Conference on bioinformatics, GCB 2017. All rights reserved.

关键词： Big data

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Virtual high throughput screening (vHTS) on an optical high speed testbed

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6th Annual HealthGrid Conference - Global HealthGrid: E-Science Meets Biomedical Informatics, HealthGrid 2008

作者： Shahid, Mohammad Ziegler, Wolfgang Kasam, Vinod Zimmermann, Marc Hofmann-Apitius, Martin Fraunhofer Institute SCAI Department of Bioinformatics Germany Bonn-Aachen International Center for Information Technology Bonn Germany

ISBN: (纸本)9781586038748

Malaria remains a global health concern, which kills over a million people each year. In this paper we present work extending the approach of the WISDOM initiative by focusing on the problems noticed during the first WISDOM challenge against malaria and test the newly established, high bandwidth optical Grid environment VIOLA for advanced bioinformatics applications using the UNICORE middleware service. In addition we present an approach to reduce the size of the compound database to improve the efficiency of the screening. © 2008 The authors and IOS Press. All rights reserved.

关键词： Diseases

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Predicting dementia in people with Parkinson’s disease

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npj Parkinson's Disease 2025年第1期11卷 1-10页

作者： Aborageh, Mohamed Hähnel, Tom Martins Conde, Patricia Klucken, Jochen Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Augustin Sankt 53757 Germany Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Luxembourg Bonn-Aachen International Center for Information Technology (B-IT) Rheinische Friedrich-Wilhelms-Universität Bonn Bonn 53115 Germany

Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson’s dementia (PDD) post-diagnosis, further validated by data from the Parkinson’s Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson’s patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention. © The Author(s) 2025.

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PEMT: a patent enrichment tool for drug discovery (vol 39, btac716, 2023)

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bioinformatics 2023年第12期39卷 btac716页

作者： Gadiya, Yojana Zaliani, Andrea Gribbon, Philip Hofmann-Apitius, Martin Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) Hamburg 22525 Germany. Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) Frankfurt 60590 Germany. Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin 53754 Germany. Bonn-Aachen International Center for Information Technology (B-IT) University of Bonn Bonn 53113 Germany.

MOTIVATION:Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources. RESULTS:Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of international Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes. AVAILABILITY AND IMPLEMENTATION:PEMT is an open-source Python tool and its source code and PyPi package are available at https://***/Fraunhofer-ITMP/PEMT and https://***/project/PEMT/, respectively. SUPPLEMENTARY INFORMATION:Supplementary data are available at bioinformatics online.

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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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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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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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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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