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Method for automated detection of outliers in crash simulations

INTERNATIONAL JOURNAL OF CRASHWORTHINESS 2023年第1期28卷 96-107页

作者： Kracker, David Dhanasekaran, Revan Kumar Schumacher, Axel Garcke, Jochen Dr Ing HcF Porsche AG Body Engn CAE Vehicle Safety Weissach Germany Univ Wuppertal Fac Mech Engn & Safety Engn Chair Optimizat Mech Struct Wuppertal Germany Fraunhofer Ctr Machine Learning St Augustin Germany Fraunhofer Inst Algorithms & Sci Comp SCAI St Augustin Germany Univ Bonn Inst Numer Simulat Bonn Germany

Stricter legal requirements in crash safety lead to more complex development processes in computer-aided engineering and result in an increasing number of simulations. Both, the construction of the simulation models as well as their evaluation are costly and time-consuming. Therefore, an automated workflow is required that significantly facilitates the analysis of the results by the engineer and increases the quality of the evaluation. In this study an automated evaluation process is proposed that detects anomalous crash behaviour in a bundle of crash simulations. The individual states from the simulation are analysed separately from each other and an outlier score is calculated using a kth-nearest-neighbour approach. Subsequently, these results are averaged into one score for each simulation. With the help of different statistical methods, a threshold value is calculated, from which a simulation can be identified as an outlier. The evaluation is carried out on 5 datasets. On average, the precision and recall of the presented method are 1.0 and 0.91, respectively.

关键词： Crash simulation analysis outlier detection machine learning process automation

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Transformer models in biomedicine

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第1期24卷 1页

作者： Madan, Sumit Lentzen, Manuel Brandt, Johannes Rueckert, Daniel Hofmann-Apitius, Martin Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Schloss Birlinghoven Dept Bioinformat D-53757 St Augustin Germany Univ Bonn Inst Comp Sci D-53115 Bonn Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53115 Bonn Germany Tech Univ Munich Sch Med Klinikum Rechts Isar Munich Germany Tech Univ Munich Sch Computat Informat & Technol Munich Germany Imperial Coll London Dept Comp London England

Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and more attention for processing various kinds of sequential data, including biological sequences and structured electronic health records. Along with this development, transformer-based models such as BioBERT, MedBERT, and MassGenie have been trained and deployed by researchers to answer various scientific questions originating in the biomedical domain. In this paper, we review the development and application of transformer models for analyzing various biomedical-related datasets such as biomedical textual data, protein sequences, medical structured-longitudinal data, and biomedical images as well as graphs. Also, we look at explainable AI strategies that help to comprehend the predictions of transformer-based models. Finally, we discuss the limitations and challenges of current models, and point out emerging novel research directions.

关键词： Transformer Biomedicine Life science Deep learning Neural networks Machine learning

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Graph-based Genetic Programming 22

Graph-based Genetic Programming

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Genetic and Evolutionary computation Conference (GECCO)

作者： Kalkreuth, Roman Sotto, Leo Francoso Dal Piccol Vasicek, Zdenek TU Dortmund Univ Dortmund Germany Fraunhofer Inst Algorithms & Sci Comp St Augustin Germany Brno Univ Technol Brno Czech Republic

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Interatomic-Potential-Free, Data-Driven Molecular Dynamics

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compUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023年 415卷

作者： Bulin, J. Hamaekers, J. Ariza, M. P. Ortiz, M. Fraunhofer Inst Algorithms & Sci Comp Schloss Birlinghoven D-53757 St Augustin Germany Univ Seville Escuela Tecn Super Ingn Seville 41092 Spain Univ Bonn Hausdorff Ctr Math Endenicher Allee 60 D-53115 Bonn Germany CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e. g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i. e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Data-Driven computing Molecular dynamics Optimal control Game theory Wasserstein metric

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Feasibility study on integrating morphological and transcriptomic data for identifying teratogenic molecular markers in zebrafish embryo toxicity testing

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compUTATIONAL TOXICOLOGY 2025年 34卷

作者： Wehr, Matthias M. Witters, Hilda Remy, Silvie Schultz, Bruce Jacobs, Marc Escher, Sylvia E. Fraunhofer Inst Toxicol & Expt Med ITEM Hannover Germany VITO Flemish Inst Technol Res NV HLTH Mol Belgium Fraunhofer Inst Algorithms & Sci Comp SCAI St Augustin Germany

To create a resource for the integration of developmental toxicity new approach methodologies (NAM) data, zebrafish embryo toxicity test (ZET) data were curated and the zeTera database was created. To capture observations of the morphological alteration potential of chemicals, the zeTera database contains experimental study designs and morphological observation data from the literature. Observations of alterations recorded in zeTera were mapped to ontologies and terms were harmonized. In addition, public transcriptomics repositories were mined for data on zebrafish embryos under chemically induced stress. The re-analyzed datasets were compiled into the zetOmics database for the identification of biomarkers of teratogenicity. To identify data-rich compounds, an overlap of both databases was formed, and compounds were grouped based on structural similarities. To identify the molecular drivers of teratogenic toxicants, Triadimefon was chosen as model compound for its well-documented teratogenic effects and known mode of action (MOA). We have compiled existing data about Triadimefon from zeTera and conducted additional testing using the ZET, with additional gene expression measurements for data gap filling. From the literature search we identified the adverse outcome pathway (AOP) of triadimefon leading to craniofacial malformations by disruption of retinoic acid metabolism. Transcriptomic response in a concentration dependent manner was observed as early as 24 h post fertilization (hpf) with consistent, statistically significant, differential expression spanning the later timepoints. A set of 5 genes (cyp26a1, dhrs3b, cyp26b1, cthrc1a, and cd248b) were selected for their differential expression pattern across time and concentration. These biomarkers were further confirmed using read across approach including data from related structures.

关键词： Zebrafish embryo toxicity Morphological alteration Teratogenicity Transcriptomics Data mining

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A hybrid approach for identifying drug repurposing candidates and their mechanisms: An interview with Vanessa Lage-Rupprecht and two co-authors

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PATTERNS 2022年第3期3卷 100466页

作者： Lage-Rupprecht, Vanessa Schultz, Bruce Namysl, Marcin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 Schloss Birlinghoven Sankt Augustin Germany Fraunhofer Inst Intelligent Anal & Informat Syst NetMedia Dept D-53757 Schloss Birlinghoven Sankt Augustin Germany

Senior researcher Vanessa Lage-Rupprecht and two collaborators talk about what data science means to them and illustrate how they managed to create a data and lab coexistence in their drug-repurposing project, which was recently published in Patterns. In this article, they have developed a drug-target-mechanism-oriented data model, Human Brain PHARMACOME, and have presented it as a resource to the community.

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Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

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BMC BIOINFORMATICS 2022年第1期23卷 231-231页

作者： Figueiredo, Rebeca Queiroz del Ser, Sara Diaz Raschka, Tamara Hofmann-Apitius, Martin Kodamullil, Alpha Tom Mubeen, Sarah Domingo-Fernandez, Daniel Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat D-53757 St Augustin Germany Rhein Friedrich Wilhelm Univ Bonn Bonn Aachen Int Ctr IT D-53115 Bonn Germany Fraunhofer Ctr Machine Learning St Augustin Germany Enveda Biosci Boulder CO 80301 USA

Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein-protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https://***/record/5831786 and https://***/ContNeXt/, respectively and developed ContNeXt (https://***/), a web application to explore the networks generated in this work.

关键词： Transcriptomic Biological context Co-expression networks Gene expression Network biology

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A Partition of Unity construction of the stabilization function in Nitsche's method for variational problems

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compUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 426卷

作者： Recio, Pablo Jimenez Schweitzer, Marc Alexander Univ Bonn Inst Numer Simulat Friedrich Hirzebruch Allee 7 D-53115 Bonn Germany Fraunhofer Inst Algorithms & Sci Comp D-53757 St Augustin Germany

In this paper we develop a partition -of -unity construction of the stabilization function required in Nitsche's method, which can be seen as a generalization of the element -wise construction that is widely used in finite element methods. This allows for the use of Nitsche's method within the Partition of Unity Method with a stabilization function that is not simply a constant over the whole boundary. In addition to that, we introduce a patch -aggregation approach designed to avoid arbitrarily large values of the stabilization function and the associated ill -conditioned systems and deteriorated convergence rates. We present numerical results to validate the proposed methods, covering Dirichlet boundary conditions, interface constraints and higherorder problems. These results clearly show that our approach leads to optimal convergence rates.

关键词： Meshfree method Partition of unity method Essential boundary conditions Nitsche's method

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A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism

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PATTERNS 2022年第3期3卷 100433页

作者： Lage-Rupprecht, Vanessa Schultz, Bruce Dick, Justus Namysl, Marcin Zaliani, Andrea Gebel, Stephan Pless, Ole Reinshagen, Jeanette Ellinger, Bernhard Ebeling, Christian Esser, Alexander Jacobs, Marc Claussen, Carsten Hofmann-Apitius, Martin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 Schloss Birlinghoven Sankt Augustin Germany Fraunhofer Inst Intelligent Anal & Informat Syst NetMedia Dept D-53757 Schloss Birlinghoven Sankt Augustin Germany Fraunhofer Inst Translat Med & Pharmacol ITMP ScreeningPort D-22525 Hamburg Germany Fraunhofer Cluster Excellence Immune Mediated Dis ScreeningPort D-22525 Hamburg Germany

The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau). One of the upstream modulators of pTau, HDAC6, was screened with 5,632 drugs in a tau-specific assay, resulting in the identification of 20 repurposing candidates. Four compounds and their known targets were found to have a link to AD-specific genes. Our approach can be applied to a variety of AD-associated pathophysiological mechanisms to identify more repurposing candidates.

关键词： drug repurposing Alzheimer disease HDAC6 tau phosphorylation knowledge graph data integration computational modeling in vitro screening

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On the influence of several factors on pathway enrichment analysis

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BRIEFINGS IN BIOINFORMATICS 2022年第3期23卷 bbac143-bbac143页

作者： Mubeen, Sarah Kodamullil, Alpha Tom Hofmann-Apitius, Martin Domingo-Fernandez, Daniel Univ Bonn Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 St Augustin Germany Fraunhofer Inst SCAI Appl Semant St Augustin Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol Bonn Germany Fraunhofer Inst SCAI Dept Bioinformat St Augustin Germany Enveda Biosci Boulder CO USA

Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors.

关键词： pathway enrichment gene set analysis pathway database omics data benchmark gene set collection

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