Machine Learning to Support the Presentation of Complex Pathway Graphs
| Autor: | Fintan McGee, Sune Steinbjorn Nielsen, Simone Zorzan, David Hoksza, Marek Ostaszewski |
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| Rok vydání: | 2021 |
| Předmět: |
Support Vector Machine
Process (engineering) Computer science 0206 medical engineering 02 engineering and technology Machine learning computer.software_genre Models Biological Task (project management) Machine Learning Kernel (linear algebra) Graph drawing Genetics Humans business.industry Applied Mathematics Node (networking) Computational Biology Visualization Support vector machine Data Display Task analysis Artificial intelligence business computer 020602 bioinformatics Signal Transduction Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18:1130-1141 |
| ISSN: | 2374-0043 1545-5963 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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