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Trends in Online Biomonitoring Read chapter Read first chapter

2018 | OriginalPaper | Chapter

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

Authors : Andras Gezsi, Bence Bruncsics, Gabor Guta, Peter Antal

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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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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Metadata
Title
Constructing a Quantitative Fusion Layer over the Semantic Level for Scalable Inference
Authors
Andras Gezsi
Bence Bruncsics
Gabor Guta
Peter Antal
Copyright Year
2018
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-319-78722-0

Electronic ISBN: 978-3-319-78723-7

Copyright Year: 2018

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

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