Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS

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  • Publication
    Quantum Feature Selection
    ( 2022-03-24)
    Mücke, Sascha
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    Heese, Raoul
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    Müller, Sabine
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    Wolter, Moritz
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    Piatkowski, Nico
    In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher- quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark datasets. We observe competitive performance.
  • Publication
    Integration of Structured Biological Data Sources using Biological Expression Language
    ( 2019-05-08)
    Hoyt, Charles Tapley
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    Domingo Fernández, Daniel
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    Mubeen, Sarah
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    Llaó, Josep Marin
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    Konotopez, Andrej
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    Ebeling, Christian
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    Birkenbihl, Colin
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    Muslu, Özlem
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    English, Bradley
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    Müller, Simon
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    Lacerda, Mauricio Pio De
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    Ali, Mehdi
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    Colby, Scott
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    Türei, Dénes
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    Palacio-Escat, Nicolàs
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    Hofmann-Apitius, Martin
    Background: The integration of heterogeneous, multiscale, and multimodal knowledge and data has become a common prerequisite for joint analysis to unravel the mechanisms and aetiologies of complex diseases. Because of its unique ability to capture this variety, Biological Expression Language (BEL) is well suited to be further used as a platform for semantic integration and harmonization in networks and systems biology. Results: We have developed numerous independent packages capable of downloading, structuring, and serializing various biological data sources to BEL. Each Bio2BEL package is implemented in the Python programming language and distributed through GitHub (https://github.com/bio2bel) and PyPI. Conclusions: The philosophy of Bio2BEL encourages reproducibility, accessibility, and democratization of biological databases. We present several applications of Bio2BEL packages including their ability to support the curation of pathway mappings, integration of pathway databases, and machine learning applications.
  • Publication
    Informed Machine Learning - A Taxonomy and Survey of Integrating Knowledge into Learning Systems
    ( 2019-03-29)
    Rüden, Laura von
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    Mayer, Sebastian
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    Beckh, Katharina
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    Georgiev, Bogdan
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    Giesselbach, Sven
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    Heese, Raoul
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    Kirsch, Birgit
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    Pfrommer, Julius
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    Pick, Annika
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    Ramamurthy, Rajkumar
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    Schuecker, Jannis
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    Garcke, Jochen
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    Bauckhage, Christian
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    Walczak, Michal
    Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process, which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. First, we provide a definition and propose a concept for informed machine learning, which illustrates its building blocks and distinguishes it from conventional machine learning. Second, we introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Third, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.