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

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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

Integrating Cloud computing, Bayesian Optimization, and Neural-Additive Modeling for Enhanced CAM Systems in 5-Axis Milling

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Procedia CIRP 2024年 128卷 532-537页

作者： Viktor Rudel Georg Vinogradov Philipp Ganser Thomas Bergs Christopher Vahl Markus Frings Valentina König Maximilian Schambach Stefan Dietzel Michael Königs Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 53757 Sankt Augustin Germany ModuleWorks GmbH Henricistraße 50 52072 Aachen Germany Merantix Momentum Merantix AI Campus Max-Urich-Straße 3 13355 Berlin Germany EXAPT Systemtechnik GmbH Theaterstraße 12 52062 Aachen Germany

This publication introduces the development and application of an advanced CAD/CAM/CAE system that leverages the computational capabilities of an edge-cloud infrastructure. The developed system consists of containerized technology models for various complex process planning simulation tasks in 5-axis-milling, such as toolpath calculation, cutter-workpiece-engagement, uncut chip geometry or cutting force simulations. Moreover, a Bayesian optimization (BO) algorithm is coupled with the system enabling multi-objective optimizations of the considered machining operations by varying predefined CAM-parameters. The result of the optimization consists of a set of Pareto-efficient solutions. Each solution realizes a different tradeoff between the technological objectives of the process planner. Since mastering the complexity of the design space is a major challenge in today’s CAM programming workflow, a Neural-Additive Model (NAM) is coupled with the system improving guided search through the configuration/result space. This reduces the convergence time to an optimal CAM parameter set. The coupling of the cloud computing, multi-objective optimization and artificial intelligence method with a CAM-kernel is demonstrated based on the process design of 5-axis milling operations for a blade-integrated disk (blisk).

关键词： Computer-aided Manufacturing systems (CAM-Systems) Multi-objective optimization Design methodology tools technologies

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

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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

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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

Towards context in large scale biomedical knowledge graphs

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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

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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

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

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SSRN

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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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

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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