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Context mining and graph queries on giant biomedical knowledge graphs

KNOWLEDGE AND INFORMATION SYSTEMS 2022年第5期64卷 1239-1262页

作者： Doerpinghaus, Jens Stefan, Andreas Schultz, Bruce Jacobs, Marc Fed Inst Vocat Educ & Training BIBB Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCA1 St Augustin Germany Bonn Rhein Sieg Univ Appl Sci Dept Comp Sci St Augustin Germany German Ctr Neurodegenerat Dis DZNE Bonn 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. Classical approaches use RDF triple stores, which have serious limitations. 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 Biological Expression Language. 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. Storing and querying a giant knowledge graph as a labeled property graph is still a technological challenge. Here, we demonstrate how our data model is able to support the understanding and interpretation of biomedical data. We present several real-world use cases that utilize our massive, generated knowledge graph derived from PubMed data and enriched with additional contextual data. Finally, we show a working example in context of biologically relevant information using SCAIView.

关键词： Current research information systems Knowledge graphs Graph embeddings Semantic search Complexity NLP Graph theory

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Determining Lennard-Jones Parameters Using Multiscale Target Data through Presampling-Enhanced, Surrogate-Assisted Global Optimization

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2023年第7期63卷 1872-1881页

作者： Muller, Max Hagg, Alexander Strickstrock, Robin Hulsmann, Marco Asteroth, Alexander Kirschner, Karl N. Reith, Dirk Bonn Rhein Sieg Univ Appl Sci Dept Comp Sci D-53757 St Augustin Germany Bonn Rhein Sieg Univ Appl Sci Dept Elect Engn Mech Engn & Tech Journalism D-53757 St Augustin Germany Schloss Birlinghoven Fraunhofer Inst Algorithms & Sci Comp SCAI D-53757 St Augustin Germany

Force field-based models are a Newtonian mechanics approximation of reality and are inherently noisy. Coupling models from different molecular scale domains (including single, gas-phase molecules up to multimolecule, condensed phase ensembles) is difficult, which is also the case for finding solutions that transfer well between the scales. In this contribution, we introduce a surrogate-assisted algorithm to optimize Lennard-Jones parameters for target data from different scale domains to overcome the difficulties named above. Specifically, our approach combines a surrogate-assisted global evolutionary optimization method with a presampling phase that takes advantage of one scale domain being less computationally expensive to evaluate. The algorithm's components were evaluated individually, elucidating their individual merits. Our findings show that the process of parametrizing force fields can significantly benefit from both the presampling method, which alleviates the need to have a good initial guess for the parameters, and the surrogate model, which improves efficiency.

关键词： algorithms Computer simulations Energy Molecular mechanics Optimization

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Unraveling progression subtypes in people with Huntington's disease

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EPMA JOURNAL 2024年第2期15卷 275-287页

作者： Raschka, Tamara Li, Zexin Gassner, Heiko Kohl, Zacharias Jukic, Jelena Marxreiter, Franz Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr IT Friedrich Hirzebruch Allee 6 D-53115 Bonn Germany Friedrich Alexander Univ Erlangen Nurnberg Univ Hosp Erlangen Dept Mol Neurol D-91054 Erlangen Germany Fraunhofer Inst Integrated Circuits IIS Fraunhofer IIS Wolfsmantel 33 D-91058 Erlangen Germany Univ Regensburg Dept Neurol Regensburg Germany Passauer Wolf Ctr Movement Disorders D-93333 Bad Gogging Germany Friedrich Alexander Univ Erlangen Nurnberg Univ Hosp Erlangen Ctr Rare Dis Erlangen ZSEER D-91054 Erlangen Germany

Background Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient *** Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up *** Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit *** In summary, this study aims toward

关键词： Huntington's disease Progression Artificial intelligence Cognition Non-motor symptoms Predictive preventive personalized medicine Patient stratification Precision medicine

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Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease

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NPJ DIGITAL MEDICINE 2024年第1期7卷 235页

作者： Brzenczek, Cyril Klopfenstein, Quentin Haehnel, Tom Froehlich, Holger Glaab, Enrico Univ Luxembourg Biomed Data Sci Grp Luxembourg Ctr Syst Biomed LCSB Esch Sur Alzette Luxembourg Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat St Augustin Germany TUD Dresden Univ Technol Med Fac Dept Neurol Dresden Germany TUD Dresden Univ Technol Univ Hosp Carl Gustav Carus Dresden Germany Univ Bonn Bonn Aachen Int Ctr IT b it Bonn Germany

Parkinson's disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data's utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83-92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.

关键词： Neurodegenerative diseases

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A DIMENSION-OBLIVIOUS DOMAIN DECOMPOSITION METHOD BASED ON SPACE-FILLING CURVES

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2023年第2期45卷 A369-A396页

作者： Griebel, Michael Schweitzer, Marc A. Troska, Lukas Univ Bonn Inst Numer Simulat D-53115 Bonn Germany Schloss Birlinghoven Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 St Augustin Germany

In this paper we present an algebraic dimension-oblivious two-level domain decomposition solver for discretizations of elliptic partial differential equations. The proposed parallel domain decomposition solver is based on a space-filling curve partitioning approach that is applicable to any discretization, i.e., it directly operates on the assembled matrix equations. Moreover, it allows for the effective use of arbitrary processor numbers independent of the dimension of the underlying partial differential equation while maintaining its optimal convergence behavior. This is the core property required to attain a combination technique solver with extreme scalability which can utilize exascale parallel systems efficiently. Moreover, this approach provides a basis for the development of a faulttolerant solver for the numerical treatment of high-dimensional Problems. To achieve the required data redundancy we are therefore concerned with large overlaps of our domain decomposition, which we construct in any dimension via space-filling curves. In this paper, we propose a space-filling curve based domain decompositoin solver and present its convergence properties and scaling behavior. The results of our numerical experiments clearly show that our approach provides optimal convergence and scaling behavior in an arbitrary dimension utilizing arbitrary processor numbers.

关键词： high-dimensional problems space-filling curves domain decomposition

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Rapid Prescreening of Organic Compounds for Redox Flow Batteries: A Graph Convolutional Network for Predicting Reaction Enthalpies from SMILES

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BATTERIES & SUPERCAPS 2021年第9期4卷 1482-1490页

作者： Barker, James Berg, Laura-Sophie Hamaekers, Jan Maass, Astrid Fraunhofer Inst Algorithms & Sci Comp Dept Virtual Mat Design D-53754 St Augustin Germany Univ Bonn Inst Numer Simulat Friedrich Hirzebruch Allee 7 D-53115 Bonn Germany Fraunhofer Ctr Machine Learning D-53757 St Augustin Germany

Identifying interesting redox-active couples from the vastness of organical chemical space requires rapid screening techniques. A good initial indicator for couples worthy of further investigation is the heat of reaction Delta H degrees. Traditional methods of calculating this quantity, both experimental and computational, are prohibitively costly at large scale. instead, we apply a graph convolutional network to estimate the heats of reaction of arbitrary redox couples orders of magnitude faster than conventional computational methods. Our graph takes only SMILES strings as input, rather than full three-dimensional geometries. A network trained on a dataset of atomization enthalpies for approximately 45,000 hydrogenization reactions, applied to a separate test set of 235 compounds and benchmarked against experimental heats of reaction, produces promisingly accurate results, and we anticipate that this methodology can be extended to other RFB-relevant reactions. However, lower predictivity for compounds in regions of chemical space not covered by the training dataset reinforces the pivotal importance of the particular chemistries presented to a model during training.

关键词： ab initio calculations electrochemistry graph convolutional network reaction enthalpies redox flow batteries

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KNOWLEDGE-BASED ADAPTATION OF PRODUCT AND PROCESS DESIGN IN BLISK MANUFACTURING

KNOWLEDGE-BASED ADAPTATION OF PRODUCT AND PROCESS DESIGN IN ...

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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) - Sustainable Energy - Accelerating the Transition by Advancing Turbine Technology

作者： Ganser, Philipp Landwehr, Markus Schiller, Sven Vahl, Christopher Mayer, Sebastian Bergs, Thomas Fraunhofer Inst Prod Technol IPT Aachen Germany Fraunhofer Inst Algorithms & Sci Comp SCAI St Augustin Germany Lab Machine Tools & Prod Engn WZL Aachen Germany

ISBN: (纸本)9780791885000

Early and efficient harmonization between product design and manufacturing represents one of the most challenging tasks in engineering. Concepts such as simultaneous engineering aim for a product creation process, which addresses both, functional requirements as well as requirements from production. However, existing concepts mostly focus on organizational tasks and heavily rely on the human factor for the exchange of complex information across different domains, organizations or systems. Nowadays product and process design make use of advanced software tools such as computer-aided design, manufacturing and engineering systems (CAD/CAM/CAE). Modern systems already provide a seamless integration of both worlds in a single digital environment to ensure a continuous workflow. Yet, for the holistic harmonization between product and process design, the following aspects are missing:. The digital environment does not provide a complete and data consistent digital twin of the component;this applies especially to the process design and analysis environment. Due to the lack of process and part condition data in the manufacturing environment an adaptation of product and process design for a balanced functionality and manufacturability is hindered. Systematic long-term data analytics across different product and process designs with the ultimate goal to transfer knowledge from one product to the next and to accelerate the entire product development process is not considered This paper presents an exploration concept which couples product design (CAD), process design (CAM), process simulation (CAE) and process adaptation in a single software system. The approach provides insights into correlations and dependencies between input parameters of product/process design and the process output. The insights potentially allow for a knowledge-based adaptation, tackling well-known optimization issues such as parameter choice or operation sequencing. First results are demonstrated using t

关键词： Milling Blisk CAD CAM CAE Digital Twin Digital Environment Exploration Facilitated Variation Manufacturing Variations Modeling Turbomachinery

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STonKGs: a sophisticated transformer trained on biomedical text and knowledge graphs

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BIOINFORMATICS 2022年第6期38卷 1648-1656页

作者： Balabin, Helena Hoyt, Charles Tapley Birkenbihl, Colin Gyori, Benjamin M. Bachman, John Kodamullil, Alpha Tom Ploeger, Paul G. Hofmann-Apitius, Martin Domingo-Fernandez, Daniel Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat D-53757 St Augustin Germany Univ Appl Sci Dept Bonn Rhein Sieg D-53757 St Augustin Germany Harvard Med Sch Lab Syst Pharmacol Boston MA 02115 USA Fraunhofer Ctr Machine Learning St Augustin Germany Enveda Biosci Boulder CO 80301 USA

Motivation: The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models. However, representations based on a single modality are inherently limited. Results: To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e. text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e. from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications.

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GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第1期19卷 491-500页

作者： Muslu, Oezlem Hoyt, Charles Tapley Lacerda, Mauricio Hofmann-Apitius, Martin Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Business Area Bioinformat D-53754 Schloss Birlinghoven Sankt Augustin Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53115 Bonn Germany UCB Biosci GmbH Alfred Nobel Str 1040789 Monheim Germany

The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://***/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target

关键词： Artificial intelligence neural networks bioinformatics

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A metal hydride system for a forklift: Feasibility study on on-board chemical storage of hydrogen using numerical simulation

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第24期47卷 12240-12250页

作者： Bedrunka, Mario Bornemann, Nils Steinebach, Gerd Reith, Dirk Bonn Rhein Sieg Univ Appl Sci Inst Technol Resource & Energy Efficient Engn Grantham Allee 20 D-53757 St Augustin Germany GKN Sinter Met Engn GmbH Krebsoege 10 D-42477 Radevormwald Germany Schloss Birlinghoven Fraunhofer Inst Algorithms & Sci Comp SCAI D-53757 St Augustin Germany

This paper describes a technical feasibility study of on-board metal hydride storage systems. The main advantages of these systems would be that of being able to replace counterweights with the weight of the storage system and using the heat emissions of fuel cells for energy, making forklifts a perfect use case. The main challenge is designing a system that supplies the required energy for a sufficiently long period. A first draft was set up and analyzed to provide a forklift based on a fuel cell with hydrogen from HydralloyC5 or FeTiMn. The primary design parameter was the required amount of stored hydrogen, which should provide energy equal to the energy capacity of a battery in an electric vehicle. To account for highly dynamic system requirements, the reactor design was optimized such that the storage was charged in a short time. Additionally, we investigated a system in which a fixed amount of hydrogen energy was required. For this purpose, we used a validated simulation model for the design concepts of metal hydride storage systems. The model includes all relevant terms and parameters to describe processes inside the system's particular reactions and the thermal conduction due to heat exchangers. We introduce an embedded fuel cell model to calculate the demand for hydrogen for a given power level. The resulting calculations provide the required time for charging or a full charge depending on the tank's diameter and, therefore, the necessary number of tanks. We conclude that the desired hydrogen supply times are given for some of the use cases. Accordingly, the simulated results suggest that using a metal hydride system could be highly practical in forklifts. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Metal hydride storage Simulations Forklifts Hydrogen storage

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