检索结果-内蒙古大学图书馆

Semantic knowledge graph embeddings for biomedical research: data integration using linked open data 15

学校读者我要写书评

暂无评论

Semantic knowledge graph embeddings for biomedical research:...

15th International Conference on Semantic Systems, SEMPDS 2019

作者： Dörpinghaus, Jens Jacobs, Marc Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany

Knowledge Graphs are becoming a key instrument for biomedical knowledge discovery and modeling. These approaches rely on structured data, e.g. about related proteins or genes, and form cause-and-effect networks or - if enriched with literature data and other linked data sources - knowledge graphs. A key aspect of analysis on these graphs is the missing context. Here we present a novel semantic approach towards a context enriched Knowledge Graph for biomedical research utilizing data integration with linked data. The result is a general graph concept that can be used for graph embeddings in different contexts or layers. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge graph

Predicting Properties of Oxide Glasses Using Informed Neural Networks

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Maier, Gregor Hamaekers, Jan Martilotti, Dominik-Sergio Ziebarth, Benedikt Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany Institut für Numerische Simulation Universität Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven Sankt Augustin53757 Germany Schott AG Hattenbergstrasse 10 Mainz55122 Germany

Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young’s modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models. © 2023, CC BY-SA.

关键词： Extrapolation

Sustainability nexus AID: infrastructure resilience

学校读者我要写书评

暂无评论

Sustainability Nexus Forum 2024年第1期32卷 1-7页

作者： Erfani, Tohid Naeini, Mohammad Mortazavi Borgomeo, Edoardo Anvari, Mehrnaz Hurford, Anthony Erfani, Rasool Zarei, Azin Matin, Mir A. Madani, Kaveh Department of Civil Environmental and Geomatic Engineering University College London London UK Department of Planning Industry and Environment Parramatta Australia Department of Engineering University of Cambridge Cambridge UK Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Environment Agency Wales UK Department of Engineering Manchester Metropolitan University Manchester UK United Nations University Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES) Dresden Germany United Nations University Institute for Water Environment and Health (UNU-INWEH) Richmond Hill Canada

Infrastructure resilience advanced through nexus thinking is pivotal for societies to handle disruptions and ensure sustainable functionality. This interconnected approach understands infrastructure as an interdependent complex system and enables cooperative planning to achieve resilience. However, challenges like data inadequacy, financial limitations and governance issues impede its adoption, especially in developing regions. The United Nations University (UNU) Sustainability Nexus Analytics, Informatics and Data (AID) Programme strives to promote integrated resource management for sustainable development and fulfilling the UN 2030 Agenda. Through its Infrastructure Resilience Module, the initiative provides tools, data platforms and localised capacity building to empower professionals and communities for evidence-based, collaborative decision-making accounting for intersectoral relationships. By supporting context-specific analytical capabilities, bridging data gaps, and governance silos, the programme aims to pave the way for resilient and sustainable infrastructure development, particularly across vulnerable regions in the Global South, which face disproportionate infrastructure service disruptions.

关键词：

Towards context in large scale biomedical knowledge graphs

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Dörpinghaus, Jens Stefan, Andreas Schultz, Bruce Jacobs, Marc Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin Germany

Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further query and discovery approaches. Here, we propose a multiple step knowledge graph approach using labeled property graphs based on polyglot persistence systems to utilize context data for context mining, graph queries, knowledge discovery and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof-of-concept based on biomedical literature and text mining. Our test system contains a knowledge graph derived from the entirety of PubMed and SCAIView data and is enriched with text mining data and domain specific language data using BEL. Here, context is a more general concept than annotations. This dense graph has more than 71M nodes and 850M relationships. We discuss the impact of this novel approach with 27 real world use cases represented by graph queries. Copyright © 2020, The Authors. All rights reserved.

关键词： Data mining

Optimization of retrieval algorithms on large scale knowledge graphs

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Dörpinghaus, Jens Stefan, Andreas Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin Germany

Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Although a lot of research has been done on the field of query optimization, query transformation and of course in storing and retrieving large scale knowledge graphs the field of algorithmic optimization is still a major challenge and a vital factor in using graph databases. Few researchers have addressed the problem of optimizing algorithms on large scale labeled property graphs. Here, we present two optimization approaches and compare them with a naive approach of directly querying the graph database. The aim of our work is to determine limiting factors of graph databases like Neo4j and we describe a novel solution to tackle these challenges. For this, we suggest a classification schema to differ between the complexity of a problem on a graph database. We evaluate our optimization approaches on a test system containing a knowledge graph derived biomedical publication data enriched with text mining data. This dense graph has more than 71M nodes and 850M relationships. The results are very encouraging and – depending on the problem – we were able to show a speedup of a factor between 44 and 3839. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Bulin, Johannes Hamaekers, Jan Department of Virtual Material Design Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven Sankt Augustin53757 Germany

Determining whether two particle systems are similar is a common problem in particle simulations. When the comparison should be invariant under permutations, orthogonal transformations, and translations of the systems, special techniques are needed. We present an algorithm that can test particle systems of finite size for similarity and, if they are similar, can find the optimal alignment between them. Our approach is based on an invariant version of the root mean square deviation (RMSD) measure and is capable of finding the globally optimal solution in O(n3) operations where n is the number of three-dimensional particles. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Sparse tensor product approximation for a class of generalized method of moments estimators

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Gilch, Alexandros Griebel, Michael Oettershagen, Jens Institute for Numerical Simulation Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

Generalized Method of Moments (GMM) estimators in their various forms, including the popular Maximum Likelihood (ML) estimator, are frequently applied for the evaluation of complex econometric models with not analytically computable moment or likelihood functions. As the objective functions of GMM-and ML-estimators themselves constitute the approximation of an integral, more precisely of the expected value over the real world data space, the question arises whether the approximation of the moment function and the simulation of the entire objective function can be combined. Motivated by the popular Probit and Mixed Logit models, we consider double integrals with a linking function which stems from the considered estimator, e.g. the logarithm for Maximum Likelihood, and apply a sparse tensor product quadrature to reduce the computational effort for the approximation of the combined integral. Given Hölder continuity of the linking function, we prove that this approach can improve the order of the convergence rate of the classical GMM-and ML-estimator by a factor of two, even for integrands of low regularity or high dimensionality. This result is illustrated by numerical simulations of Mixed Logit and Multinomial Probit integrals which are estimated by ML-and GMM-estimators, *** Codes 62P20, 65D30, 65D32 © 2020, CC BY.

关键词： Method of moments

NFDI4Health workflow and service for synthetic data generation, assessment and risk management

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Moazemi, Sobhan Adams, Tim Ng, Hwei Geok Kühnel, Lisa Schneider, Julian Näher, Anatol-Fiete Fluck, Juliane Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Germany ZB MED – Information Centre for Life Sciences Germany Faculty of Technology Bielefeld University Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Germany Institute of Medical Informatics Charité–Universitätsmedizin Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Germany The Agricultural Faculty University of Bonn Germany Institute for Geodesy and Geoinformation University of Bonn Germany University of Bonn Germany

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI);however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany’s National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch Institute (RKI). © 2024, CC BY-SA.

关键词： Electronic health record

Resonant Solitary States in Complex Networks

学校读者我要写书评

暂无评论

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