In 2020, around 44% of natural gas in Germany was used in combined heat and power as well as in combined cycle gas turbines plants. As district heating will play an important role in future heating planning, the retro...
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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, d...
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.
While projects, developments and applications addressing and using artificial intelligence (AI) are rather multifaceted and their number is constantly increasing, the standardisation activities in the field of artific...
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While projects, developments and applications addressing and using artificial intelligence (AI) are rather multifaceted and their number is constantly increasing, the standardisation activities in the field of artificial intelligence are limited, their number is significantly lower and does not increase at the same pace. The European funded project *** aims at supporting European experts' presence in and contributions to international standardisation activities in ICT. The focus of the project is on the 5 priority domains identified by the European Commission (Cloud computing, IoT, Big Data, Cyber Security, 5G) and on Artificial Intelligence while being open for other relevant topics defined in the annual European Rolling Plan for ICT Standardisation. The project has two main outcomes: (i) increased contribution of European experts in international standardisation through support by providing grants for planned contributions of successful applications of experts, and (ii) an online observatory of published standards and ongoing standardisation activities in the areas mentioned before. This observatory (called Standards Watch) is accessible through the project's web site and open for contributions and comments from registered users. As part of the effort for the Standards Watch the projects has prepared a comprehensive analysis of the international standardisation landscape in the AI field, that comprises a description of the ICT standards and ongoing work at international level in the field of AI across the standardisation organisations already active in the field. In this article we will present results of our work where we have analysed the work of the 5 international and European Standards Development Organisations (SDOs) IEEE,1 ISO/IEC,2 ITU-T,3 ETSI4 and CEN-CENELEC5 developing standards in the field of AI. The first 4 bodies have already been active several years in AI standardisation at the time of writing this article. CEN-CENELEC has launched a focu
In this report, we introduce a novel approach to visualize extremely large graphs efficiently. Our method combines two force-directed algorithms, Kamada-Kawai and ForceAtlas2, to handle different graph components base...
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We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specific data sample. In this sense, it may ...
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The quality of uni-directional tape in its production process is affected by environmental conditions like temperature and production speed. In this paper, computer vision algorithms on the scanned images are needed t...
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In (Griebel and Hamaekers, Fast discrete Fourier transform on generalized sparse grids, Sparse grids and Applications, volume 97 of Lecture Notes in Computational Science and Engineering, pages 75–108, Springer, 2014...
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Energy-based fragmentation methods approximate the potential energy of a molecular system as a sum of contribution terms built from the energies of particular subsystems. Some such methods reduce to truncations of the...
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Introduction A modern approach to ensuring privacy when sharing datasets is the use of synthetic data generation methods, which often claim to outperform classic anonymization techniques in the trade-off between data ...
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We investigate the persistence of embedded eigenvalues for a class of magnetic Laplacians on an infinite cylindrical domain. The magnetic potential is assumed to be C2 and asymptotically periodic along the unbounded d...
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