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2019 ISWC Satellite Tracks (Posters and Demonstrations, Industry, and Outrageous Ideas), ISWC 2019-Satellites

作者： Ali, Mehdi Hoyt, Charles Tapley Domingo-Fernández, Daniel Lehmann, Jens Smart Data Analytics Group University of Bonn Germany Department of Enterprise Information Systems Fraunhofer Institute for Intelligent Analysis and Information Systems Sankt Augustin and Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

PyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes’ annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert*. Copyright 2019 for this paper by its authors.

关键词： Knowledge graph

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A geometric heuristic for rectilinear crossing minimization 20

A geometric heuristic for rectilinear crossing minimization

引用

20th Workshop on Algorithm Engineering and Experiments, ALENEX 2018

作者： Radermacher, Marcel Reichard, Klara Rutter, Ignaz Wagner, Dorothea Department of Computer Science Karlsruhe Institute of Technology Germany Department of Algorithms in Bioinformatics Universität Tübingen Tübingen Germany Department of Mathematics and Computer Science TU Eindhoven Netherlands

ISBN: (纸本)9781611975055

In this paper we consider the rectilinear crossing minimization problem, i.e., we seek a straight-line drawing ? of a graph G = (V, E) with a small number of edge crossings. Crossing minimization is an active field of research [1,9]. While there is a lot of work on heuristics for topological drawings, these techniques are typically not transferable to the rectilinear (i.e., straight-line) setting. We introduce and evaluate three heuristics for rectilinear crossing minimization. The approaches are based on the primitive operation of moving a single vertex to its crossing-minimal position in the current drawing ?, for which we give an O (kn + m)2 log (kn + m)-time algorithm, where k is the degree of the vertex and n and m are the numbers of vertices and edges of the graph, respectively. In an experimental evaluation, we demonstrate that our algorithms compute straight-line drawings with fewer crossings than energy-based algorithms implemented in the Open Graph Drawing Framework [10] on a varied set of benchmark instances. All experiments are evaluated with a statistical significance level of ? = 0.05. Copyright © 2018 by SIAM.

关键词： Benchmarking

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Progression subtypes in Parkinson's disease: Results of an AI-based multi-cohort analysis

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Clinical Neurophysiology 2024年 159卷 e39-e39页

作者： Hähnel, T. Raschka, T. Sapienza, S. Klucken, J. Glaab, E. Corvol, J. Falkenburger, B. Fröhlich, H. Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany Bonn-Aachen International Center for IT University of Bonn Bonn Germany Biomedical Data Science Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Luxembourg Institute of Health (LIH) Strassen Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Sorbonne Université Paris Brain Institute – ICM Inserm CNRS Assistance Publique Hôpitaux de Paris Pitié-Salpêtrière Hospital Department of Neurology Paris France German Center for Neurodegenerative Diseases (DZNE) Tatzberg 41 01307 Dresden Germany

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A balance index for phylogenetic trees based on rooted quartets

arXiv

引用

arXiv 2018年

作者： Coronado, Tomás M. Mir, Arnau Rosselló, Francesc Valiente, Gabriel Department of Mathematics and Computer Science University of the Balearic Islands PalmaE-07122 Spain Algorithms Bioinformatics Complexity and Formal Methods Research Group Technical University of Catalonia BarcelonaE-08034 Spain

We define a new balance index for rooted phylogenetic trees based on the symmetry of the evolutive history of every set of 4 leaves. This index makes sense for multifurcating trees and it can be computed in time linear in the number of leaves. We determine its maximum and minimum values for arbitrary and bifurcating trees, and we provide exact formulas for its expected value and variance on bifurcating trees under Ford's α-model and Aldous' β-model and on arbitrary trees under the α-γ-model. Copyright © 2018, The Authors. All rights reserved.

关键词： Bifurcation (mathematics)

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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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Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification

arXiv

引用

arXiv 2018年

作者： You, Ronghui Zhang, Zihan Wang, Ziye Dai, Suyang Mamitsuka, Hiroshi Zhu, Shanfeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Centre for Computational Systems Biology School of Mathematical Sciences Shanghai Institute of Artificial Intelligence Algorithms and ISTBI Fudan University Shanghai China Bioinformatics Center Institute for Chemical Research Kyoto University Japan Department of Computer Science Aalto University Helsinki Finland

Extreme multi-label text classification (XMTC) is an important problem in the era of big data, for tagging a given text with the most relevant multiple labels from an extremely large-scale label set. XMTC can be found in many applications, such as item categorization, web page tagging, and news annotation. Traditionally most methods used bag-of-words (BOW) as inputs, ignoring word context as well as deep semantic information. Recent attempts to overcome the problems of BOW by deep learning still suffer from 1) failing to capture the important subtext for each label and 2) lack of scalability against the huge number of labels. We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label;and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels". We empirically compared the performance of AttentionXML with those of eight state-of-the-art methods over six benchmark datasets, including Amazon-3M with around 3 million labels. AttentionXML outperformed all competing methods under all experimental settings. Experimental results also show that AttentionXML achieved the best performance against tail labels among label tree-based methods. The code and datasets are available at http://***/yourh/AttentionXML . Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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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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Brain based BMIgap as a new tool to correlate obesity and neural alterations – a multicohort study

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Neuroscience Applied 2022年 1卷

作者： A. Khuntia R. Paul O.A. Andreassen F. Degenhard R. Eils J. Erdmann C. Herrmann M. Hofmann-Apitius T. Kaufmann A.T. Kodamullil S. Mucha M.M. Nöthen M.L. Pedersen A. Quintero H. Schunkert A. Sharma H. Tost L.T. Westlye Y. Zhang J. Kambeitz N. Koutsouleris 1Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University Psychiatry Munich Germany 2Max Plank Institute of Psychiatry Psychiatry Munich Germany 3Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University- Munich- Germany Department of Psychiatry and Psychotherapy München Germany 4Norwegian Centre for Mental Disorders Research NORMENT Division of Mental Health and Addiction Oslo Norway 5Institute of Human Genetics- University of Bonn School of Medicine Bonn Germany 6Health Data Science Unit Heidelberg University Hospital- and BioQuant Heidelberg Germany 7Institute for Cardiogenetics University of Lübeck Lübeck Germany 8Heidelberg University Hospital- and BioQuant Health Data Science Unit Heidelberg Germany 9Fraunhofer Institute for Algorithms and Scientific Computing SCAI Bioinformatics Sankt Augustin Germany 10Fraunhofer Institute for Algorithms and Scientific Computing SCAI- Bioinformatics Sankt Augustin Germany 11Institute for Cardiogenetics- University of Lübeck- DZHK Cardiogenetics Lübeck Germany 12Institute of Human Genetics- University of Bonn Human Genetics Bonn Germany 13University of Oslo Department of Psychology Oslo Norway 14Deutsches Herzzentrum München- Technische Universität München Department of Cardiology Munich Germany 15Heidelberg University Hospital- and BioQuant Health Data Science Unit- Heidelberg Germany 16Central Institute of Mental Health Department of Psychiatry and Psychotherapy- Mannheim Germany 17University of Cologne Department of Psychiatry and Psychotherapy- Cologne Germany 18University of Turku Department of Psychiatry- Turku Finland 19Neuroscience and Sense Organs- University of Bari Aldo Moro Department of Basic Medical Science Bari Italy 20Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Department of Neurosciences and Mental Health Milano Italy 21Institute of Mental Health- University of Birmingham Psychiatry Birmingham United Kingdom 22University of Birmingham School of Psyc

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

引用

arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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Fuzzy Criteria in Multi-objective Feature Selection for Unsupervised Learning

引用

Procedia Computer Science 2016年 102卷 51-58页

作者： Fuyu Cai Hao Wang Xiaoqin Tang Michael Emmerich Fons J. Verbeek Section Imaging and Bioinformatics LIACS Leiden University Niels Bohrweg 1 2333 CA Leiden The Netherlands Section Algorithms and Software Technology LIACS Leiden University Niels Bohrweg 1 2333 CA Leiden The Netherlands

Feature selection in which most informative variables are selected for model generation is an important step in pattern recognition. Here, one often tries to optimize multiple criteria such as discriminating power of the descriptor, performance of model and cardinality of a subset. In this paper we propose a fuzzy criterion in multi-objective unsupervised feature selection by applying the hybridized filter-wrapper approach (FC-MOFS). These formulations allow for an efficient way to pick features from a pool and to avoid misunderstanding of overlapping features via crisp clustered learning in a conventional multi-objective optimization procedure. Moreover, the optimization problem is solved by using non-dominated sorting genetic algorithm, type two (NSGA-II). The performance of the proposed approach is then examined on six benchmark datasets from multiple disciplines and different numbers of features. Systematic comparisons of the proposed method and representative non-fuzzified approaches are illustrated in this work. The experimental studies show a superior performance of the proposed approach in terms of accuracy and feasibility.

关键词： feature selection fuzzy criteria unsupervised learning multi-objective optimization

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