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PEMT: a patent enrichment tool for drug discovery (vol 39, btac716, 2023)

BIOINFORMATICS 2023年第12期39卷 btac716-btac716页

作者： Gadiya, Yojana Zaliani, Andrea Gribbon, Philip Hofmann-Apitius, Martin Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) Hamburg 22525 Germany Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) Frankfurt 60590 Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin 53754 Germany Bonn-Aachen International Center for Information Technology (B-IT) University of Bonn Bonn 53113 Germany

Motivation Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public *** Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent *** and implementation PEMT is an open-source Python tool and its source code and PyPi package are available at https://***/fraunhofer-ITMP/PEMT and https://***/project/PEMT/, *** information Supplementary data are available at Bioinformatics online.

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Uncertainty quantification using nonparametric quantile estimation and metamodeling

Uncertainty quantification using nonparametric quantile esti...

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6th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS 2012

作者： Rhein, Beate Clees, Tanja Ruschitzka, Margot University of Applied Sciences Cologne Betzdorfer Str. 2 50679 Koeln Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 53754 Sankt Augustin Germany

ISBN: (纸本)9783950353709

In many applications, particularly in the engineering field, the need to consider uncertainties is recognized. To reduce the number of necessary simulations, metamodels can be used. We present a novel method on the basis of metamodels, that allows us to model not only the deterministic responses, but also the propagation of uncertainty in the entire design space. Our procedure makes it possible to determine the robust optimum quickly with common multi-criteria optimization algorithms. The novel approach offers the possibility to include the tolerance of the metamodel in the calculation. We introduce a new class of robustness measures that characterizes the propagation of uncertainty more accurately than usual: the median as measure of central tendency and the difference between median and a high quantile q as measure of dispersion. This allows the user to adjust the degree of robustness to his wishes via q. It can handle even extremely skewed distributions in an appropriate way. For the determination of the quantiles we use a novel combination of sampling scheme and nonparametric quantile estimation. This enables a fast computation on a local level. The suitability of the proposed proceeding is proved on several examples. The applicability is demonstrated on a real life example from automotive industry.

关键词： Automotive industry

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Knowledge Detection and Discovery using Semantic Graph Embeddings on Large Knowledge Graphs generated on Text Mining Results 15

Knowledge Detection and Discovery using Semantic Graph Embed...

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15th Federated Conference on Computer Science and Information Systems, FedCSIS 2020

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

ISBN: (纸本)9788395541674

Knowledge graphs play a central role in big data integration, especially for connecting data from different domains. Bringing unstructured texts, e.g. from scientific literature, into a structured, comparable format is one of the key assets. Here, we use knowledge graphs in the biomedical domain working together with text mining based document data for knowledge extraction and retrieval from text and natural language structures. For example cause and effect models, can potentially facilitate clinical decision making or help to drive research towards precision medicine. However, the power of knowledge graphs critically depends on context information. Here we provide a novel semantic approach towards a context enriched biomedical knowledge graph utilizing data integration with linked data applied to language technologies and text mining. This graph concept can be used for graph embedding applied in different approaches, e.g with focus on topic detection, document clustering and knowledge discovery. We discuss algorithmic approaches to tackle these challenges and show results for several applications like search query finding and knowledge discovery. The presented remarkable approaches lead to valuable results on large knowledge graphs. © 2020 Polish Information Processing Society - as it is since 2011.

关键词： Knowledge graph

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Robust optimization for long running computer simulations with a focus on robustness measures 1

Robust optimization for long running computer simulations wi...

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1st International Conference on Engineering and Applied Sciences Optimization, OPT-i 2014

作者： Rhein, Beate Clees, Tanja Ruschitzka, Margot Cologne University of Applied Sciences Betzdorfer Str. 2 Koeln50679 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53754 Germany

ISBN: (纸本)9789609999465

Robust optimization determines how the input variables dispersion is propagated on the output variables. This is of great practical relevance: For example, the quality of a product is influenced decisively by production tolerances. In industrial applications it is important to characterize the range of variation with appropriate measures. The industry is particularly interested in accurate limits of the output distribution or its centered part. In this article the mathematical characterization of robustness is discussed under the viewpoint of its practical applicability. It is shown, that the usually used robustness measures mean for central tendency and standard deviation for dispersion produce inaccurate limits. Instead several measures based on quantiles are proposed. The median is used as measure of central tendency while different quantile ranges are used as measure of dispersion. They are compared for the robust optimization of mathematical functions and industrial applications. The advantages of the quantile measures are pointed out. The computation of quantiles is expensive, because it needs many function evaluations. Due to their long runtime only a few simulations can be executed in practice. A methodology tailored to this situation is proposed. It is based on the use of metamodels. Starting with a few real simulations a metamodel for the system is build. Further ones for median and dispersion of the output variables are derived from it. This enables the user to perform a full multicriteria robust optimization on the whole parameter range. The methodology takes the tolerances of the metamodels into account. A new measure for the tolerance of metamodels which are derived from metamodels is presented. It is used to estimate the accuracy of the quantile models. The tolerance can easily be integrated in the robust optimization process.

关键词： Functions

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Optimization of Retrieval algorithms on Large Scale Knowledge Graphs 15

Optimization of Retrieval Algorithms on Large Scale Knowledg...

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15th Federated Conference on Computer Science and Information Systems, FedCSIS 2020

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

ISBN: (纸本)9788395541674

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. © 2020 Polish Information Processing Society - as it is since 2011.

关键词： Knowledge graph

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Multilevel B-Spline Repulsive Energy in Nanomodeling of Graphenes

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Journal of Surface Engineered Materials and Advanced Technology 2014年第2期4卷 75-86页

作者： Maharavo Randrianarivony Virtual Material Design Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin Germany

Quantum energies which are used in applications are usually composed of repulsive and attractive terms. The objective of this study is to use an accurate and efficient fitting of the repulsive energy instead of using standard parametrizations. The investigation is based on Density Functional Theory and Tight Binding simulations. Our objective is not only to capture the values of the repulsive terms but also to efficiently reproduce the elastic properties and the forces. The elasticity values determine the rigidity of a material when some traction or load is applied on it. The pair-potential is based on an exponential term corrected by B-spline terms. In order to accelerate the computations, one uses a hierarchical optimization for the B-splines on different levels. Carbon graphenes constitute the configurations used in the simulations. We report on some results to show the efficiency of the B-splines on different levels.

关键词： Repulsive Potential B-Spline Force Elastic Stress Hierarchy

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On DFT Molecular Simulation for Non-Adaptive Kernel Approximation

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Advances in Materials Physics and Chemistry 2014年第6期4卷 105-115页

作者： Maharavo Randrianarivony Virtual Material Design Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin Germany

Using accurate quantum energy computations in nanotechnologic applications is usually very computationally intensive. That makes it difficult to apply in subsequent quantum simulation. In this paper, we present some preliminary results pertaining to stochastic methods for alleviating the numerical expense of quantum estimations. The initial information about the quantum energy originates from the Density Functional Theory. The determination of the parameters is performed by using methods stemming from machine learning. We survey the covariance method using marginal likelihood for the statistical simulation. More emphasis is put at the position of equilibrium where the total atomic energy attains its minimum. The originally intensive data can be reproduced efficiently without losing accuracy. A significant acceleration gain is perceived by using the proposed method.

关键词： DFT Energy Stochastic Covariance Hyperparameter

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Automatically selected skip edges in conditional random fields for named entity recognition

Automatically selected skip edges in conditional random fiel...

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8th International Conference on Recent Advances in Natural Language Processing, RANLP 2011

作者： Klinger, Roman Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven 53754 Sankt Augustin Germany

Incorporating distant information via manually selected skip chain templates has been shown to be beneficial for the performance of conditional random field models in contrast to a simple linear chain based structure (Sutton and McCallum, 2007;Galley, 2006;Liu et al., 2010). The set of properties to be captured by a template is typically manually chosen with respect to the application domain. In this paper, a search strategy to find meaningful skip chains independent from the application domain is proposed. From a huge set of potentially beneficial templates, some can be shown to have a positive impact on the performance. The search for a meaningful graphical structure demonstrates the usefulness of the approach with an increase of nearly 2% F1 measure on a publicly available data set (Klinger et al., 2008).

关键词： Chains

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撞车模拟的不稳定性分析(英文)

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工程数学学报 2002年第2期19卷 47-51,56页

作者：梅立泉 Thole C A. Fraunhofer-Institute of Algorithms and Scientific Computing D53754 St.Augustin Germany

众所周知 ,在并行机器上多次执行同一应用会产生不同的撞击数值模拟结果。为了研究这一问题 ,我们开发了一个工具 ,它可从PAM CRASH读取多个结果文件 ,计算各种统计量并可利用可视化工具产生这些量的输出以用于分析。

关键词：数值模拟软件稳定性分析撞车模拟并行机器撞击

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Nonlinear metamodeling and robust optimization in automotive design

Nonlinear metamodeling and robust optimization in automotive...

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作者： Clees, Tanja Nikitin, Igor Nikitina, Lialia Thole, Clemens-August Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven 53754 Sankt Augustin Germany

ISBN: (纸本)9789898425782

We overview the methods for nonlinear metamodeling of a simulation database featuring continuous exploration of simulation results, tolerance prediction, sensitivity analysis, robust multiobjective optimization and rapid interpolation of bulky FEM data. Large scatter of simulation results, in crash-test simulations caused for example by buckling, is still a challenging issue for increasing predictability of simulation and accuracy of optimization results. For industrially relevant simulations with large scatter, novel stochastic methods are introduced and their efficiency is demonstrated for benchmark cases.

关键词： Multiobjective optimization

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