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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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Privacy Risk Assessment for Synthetic Longitudinal Health Data

Privacy Risk Assessment for Synthetic Longitudinal Health Da...

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69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024

作者： Schneider, Julian Walter, Marvin Otte, Karen Meurers, Thierry Perrar, Ines Nöthlings, Ute Adams, Tim Fröhlich, Holger Prasser, Fabian Fluck, Juliane Kühnel, Lisa Knowledge Management ZB MED - Information Centre for Life Sciences Cologne Germany Medical Informatics Group Berlin Institute of Health at Charité - Universitätsmedizin Berlin Berlin Germany Institute of Nutritional and Food Sciences - Nutritional Epidemiology University of Bonn Bonn Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany The Agricultural Faculty University of Bonn Bonn Germany Faculty of Technology Bielefeld University Bielefeld Germany

ISBN: (纸本)9781643685366

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 utility and privacy. Recently, it was demonstrated that various deep learning-based approaches are able to generate useful synthesized datasets, often based on domain-specific analyses. However, evaluating the privacy implications of releasing synthetic data remains a challenging problem, especially when the goal is to conform with data protection guidelines. Methods Therefore, the recent privacy risk quantification framework Anonymeter has been built for evaluating multiple possible vulnerabilities, which are specifically based on privacy risks that are considered by the European Data Protection Board, i.e. singling out, linkability, and attribute inference. This framework was applied to a synthetic data generation study from the epidemiological domain, where the synthesization replicates time and age trends previously found in data collected during the DONALD cohort study (1312 participants, 16 time points). The conducted privacy analyses are presented, which place a focus on the vulnerability of outliers. Results The resulting privacy scores are discussed, which vary greatly between the different types of attacks. Conclusion Challenges encountered during their implementation and during the interpretation of their results are highlighted, and it is concluded that privacy risk assessment for synthetic data remains an open problem. © 2024 The Authors.

关键词： Differential privacy

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ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs 13

ProtSTonKGs: A Sophisticated Transformer Trained on Protein ...

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13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022

作者： Balabin, Helena Hoyt, Charles Tapley Gyori, Benjamin M. Bachman, John Kodamullil, Alpha Tom Hofmann-Apitius, Martin Domingo-Fernández, Daniel Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin53757 Germany Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Fraunhofer Center for Machine Learning Germany Enveda Biosciences BoulderCO80301 United States

While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowl- edge graphs, their union can better exploit the advantages of such ap- proaches, ultimately improving representations of biology. Using mul- timodal transformers for such purposes can improve performance on context dependent classification tasks, as demonstrated by our previ- ous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce Prot- STonKGs, a transformer aimed at learning all-encompassing representa- tions of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark Prot- STonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of infor- mation, paving the foundation for future approaches that use multiple modalities for biomedical applications. Copyright © 2022 for this paper by its authors.

关键词： Natural language processing systems

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Proposal of an achitecture for terminology management in a research project 30

Proposal of an achitecture for terminology management in a r...

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30th Medical Informatics Europe Conference, MIE 2020

作者： Mora, Sara Madan, Sumit Gebel, Stephan Giacomini, Mauro University of Genoa Italy Fraunhofer Institute for Algorithms and Scientific Computing SCAI Department of Bioinformatics Schloss Birlinghoven Sankt Augustin Germany

ISBN: (纸本)9781643680828

Clinical and medical knowledge evolve and this causes changes in concepts and terms that describe them. The objective of this work is to formally present an ontology-based standard architecture that will be used in the scenario of neurodegeneration research to maintain terminologies and their relations updated and coherent over the time. The proposed structure is composed by three elements that will allow the user to do a list of operations on the terminology resources explicitly contemplated by the Common Terminology Service Release 2 (CTS2). © 2020 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Ontology

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NFDI4Health Workflow and Service for Synthetic Data Generation, Assessment and Risk Management

NFDI4Health Workflow and Service for Synthetic Data Generati...

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69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024

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 Sankt Augustin Germany Knowledge Management ZB MED - Information Centre for Life Sciences Cologne Germany Faculty of Technology Bielefeld University Bielefeld Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Germany Institute of Medical Informatics Charité-Universitätsmedizin Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Berlin Germany The Agricultural Faculty University of Bonn Bonn Germany Institute for Geodesy and Geoinformation University of Bonn Bonn Germany University of Bonn Bonn Germany

ISBN: (纸本)9781643685366

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 The Authors.

关键词： Electronic health record

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On the influence of several factors on pathway enrichment analysis

arXiv

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arXiv 2022年

作者： Mubeen, Sarah Kodamullil, Alpha Tom Hofmann-Apitius, Martin Domingo-Fernández, Daniel Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany University of Bonn Bonn53115 Germany Fraunhofer Center for Machine Learning Germany Enveda Biosciences BoulderCO80301 United States

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. © 2022, CC BY.

关键词： Database systems

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Stop Oversampling for Class Imbalance Learning: A Critical Review

arXiv

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arXiv 2022年

作者： Hassanat, Ahmad B. Tarawneh, Ahmad S. Altarawneh, Ghada A. Almuhaimeed, Abdullah Faculty of Information Technology Mutah University Karak Jordan Dept. of Algorithms and Their Applications Eötvös Loránd University Budapest Hungary Dept. Accounting Mutah University Karak Jordan The National Centre for Genomics and Bioinformatics King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and three imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications. © 2022, CC BY.

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

arXiv

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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

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Portability of Scientific Workflows in NGS Data Analysis: A Case Study

arXiv

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arXiv 2020年

作者： Schiefer, Christopher Bux, Marc Brandt, Jörgen Messerschmidt, Clemens Reinert, Knut Beule, Dieter Leser, Ulf Humboldt-Universität zu Berlin Knowledge Management in Bioinformatics Berlin Germany Berlin Germany Charité - Universitätsmedizin Berlin Berlin Germany Freie Universität Berlin Algorithms in Bioinformatics Berlin Germany Max Delbrück Center for Molecular Medicine Berlin Germany

The analysis of next-generation sequencing (NGS) data requires complex computational workflows consisting of dozens of autonomously developed yet interdependent processing steps. Whenever large amounts of data need to be processed, these workflows must be executed on a parallel and/or distributed systems to ensure reasonable runtime. To simplify the development and parallel execution of workflows, researchers rely on existing services such as distributed file systems, specialized workflow languages, resource managers, or workflow scheduling tools. Systems that cover some or all of these functionalities are categorized under labels like scientific workflow management systems, big data processing frameworks, or batch-queuing systems. Porting a workflow developed for a particular system on a particular hardware infrastructure to another system or to another infrastructure is non-trivial, which poses a major impediment to the scientific necessities of workflow reproducibility and workflow reusability. In this work, we describe our efforts to port a state-of-the-art workflow for the detection of specific variants in whole-exome sequencing of mice. The workflow originally was developed in the scientific workflow system snakemake for execution on a high-performance cluster controlled by Sun Grid Engine. In the project, we ported it to the scientific workflow system SaasFee that can execute workflows on (multi-core) stand-alone servers or on clusters of arbitrary sizes using the Hadoop cluster management software. The purpose of this port was that also owners of low-cost hardware infrastructures, for which Hadoop was made for, become able to use the workflow. Although both the source and the target system are called scientific workflow systems, they differ in numerous aspects, ranging from the workflow languages to the scheduling mechanisms and the file access interfaces. These differences resulted in various problems, some expected and more unexpected, that had to be resol

关键词： Reusability

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SYNTHETIC DATA GENERATION FOR A LONGITUDINAL COHORT STUDY - EVALUATION, METHOD EXTENSION AND REPRODUCTION OF PUBLISHED DATA ANALYSIS RESULTS

arXiv

引用

arXiv 2023年

作者： Kühnel, Lisa Schneider, Julian Perrar, Ines Adams, Tim Prasser, Fabian Nöthlings, Ute Fröhlich, Holger Fluck, Juliane Knowledge Management Zb Med Information Centre for Life Sciences Cologne50931 Germany Faculty of Technology Bielefeld University Bielefeld33615 Germany Institute of Nutritional and Food Sciences - Nutritional Epidemiology University of Bonn Bonn53115 Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Scai Sankt Augustin53757 Germany Medical Informatics Group Berlin Institute of Health Charité - Universitätsmedizin Berlin Berlin10117 Germany Bonn-Aachen International Center for It University of Bonn Friedrich Hirzebruch-Allee 6 Bonn53115 Germany The Agricultural Faculty University of Bonn Bonn53115 Germany

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e. data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case. © 2023, CC BY.

关键词： Large dataset

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