Reference : Machine Learning to Support the Presentation of Complex Pathway Graphs.
Scientific journals : Article
Engineering, computing & technology : Computer science
Systems Biomedicine
http://hdl.handle.net/10993/40650
Machine Learning to Support the Presentation of Complex Pathway Graphs.
English
Nielsen, Sune, S mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > > ; Luxembourg Institute of Science & Technology - LIST > Environmental Research and Innovation (ERIN) Department]
Ostaszewski, Marek mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
McGee, Fintan [Luxembourg Institute of Science & Technology - LIST > Environmental Research and Innovation (ERIN) Department]
Hoksza, David mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Zorzan, Simone [Luxembourg Institute of Science & Technology - LIST > Environmental Research and Innovation (ERIN) Department]
2019
IEEE/ACM transactions on computational biology and bioinformatics
Yes
International
1545-5963
1557-9964
United States
[en] Network visualization ; Bioinformatics ; Machine learning
[en] Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
Researchers ; Students
http://hdl.handle.net/10993/40650
10.1109/TCBB.2019.2938501
https://ieeexplore.ieee.org/document/8821368

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