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Easy Semantification of Bioassays

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AIxIA 2021 – Advances in Artificial Intelligence (AIxIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13196))

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Abstract

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.

Supported by TIB Leibniz Information Centre for Science and Technology, the EU H2020 ERC project ScienceGRaph (GA ID: 819536) and the ITN PERICO (GA ID: 812968).

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Notes

  1. 1.

    https://www.orkg.org/.

  2. 2.

    https://bioportal.bioontology.org/.

  3. 3.

    https://github.com/MarcoAnteghini/Easy-Semantification-of-Bioassays-SM.

  4. 4.

    https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html.

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Author information

Authors and Affiliations

  1. Lifeglimmer GmbH, Markelstr. 38, 12163, Berlin, Germany

    Marco Anteghini & Vitor A. P. Martins dos Santos

  2. Wageningen University and Research, Laboratory of Systems and Synthetic Biology, Stippeneng 4, 6708 WE, Wageningen, The Netherlands

    Marco Anteghini & Vitor A. P. Martins dos Santos

  3. TIB Leibniz Information Centre for Science and Technology, Hannover, Germany

    Jennifer D’Souza & Sören Auer

Authors
  1. Marco Anteghini
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  2. Jennifer D’Souza
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  3. Vitor A. P. Martins dos Santos
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  4. Sören Auer
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Corresponding author

Correspondence to Marco Anteghini .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

  2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Francesca Gasparini

  3. Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy

    Viviana Mascardi

  4. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Matteo Palmonari

  5. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Giuseppe Vizzari

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Anteghini, M., D’Souza, J., dos Santos, V.A.P.M., Auer, S. (2022). Easy Semantification of Bioassays. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_14

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