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Constructing a Quantitative Fusion Layer over the Semantic Level for Scalable Inference

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Bioinformatics and Biomedical Engineering (IWBBIO 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10813))

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Abstract

We present a methodology and a corresponding system to bridge the gap between prioritization tools with fixed target and unrestricted semantic queries. We describe the advantages of an intermediate level of networks of similarities and relevances: (1) it is derived from raw, linked data (2) it ensures efficient inference over partial, inconsistent and noisy cross-domain, cross-species linked open data, (3) preserved transparency and decomposability of the inference allows semantic filters and preferences to control and focus of the inference, (4) high-dimensional, weakly significant evidences, such as overall summary statistics could also be used in the inference, (5) quantitative and rank based inference primitives can be defined, and (6) queries are unrestricted, e.g. prioritized variables, and (7) it allows wider access for non-technical experts. We provide a step-by-step guide for the methodology using a macular degeneration model, including drug, target and disease domains. The system and the model presented in the paper are available at bioinformatics.mit.bme.hu/QSF.

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Acknowledgments

The research has been supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.2-16-2017-00013) and by OTKA 112915. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreement No 633589. This publication reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information it contains.

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Authors and Affiliations

  1. Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary

    Andras Gezsi, Bence Bruncsics & Peter Antal

  2. Abiomics Europe Ltd., Budapest, Hungary

    Andras Gezsi, Bence Bruncsics, Gabor Guta & Peter Antal

Authors
  1. Andras Gezsi
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  2. Bence Bruncsics
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  3. Gabor Guta
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  4. Peter Antal
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Corresponding author

Correspondence to Bence Bruncsics .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Francisco Ortuño

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Gezsi, A., Bruncsics, B., Guta, G., Antal, P. (2018). Constructing a Quantitative Fusion Layer over the Semantic Level for Scalable Inference. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-78723-7_4

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