Abstract
Biological domain has been blessed with more and more data from biotechnologies as well as data integration tools. In the renaissance of machine learning and artificial intelligence, there is so much promise of data-driven biological knowledge discovery. However, it is not straight forward due to the complexity of the domain knowledge hidden in the data. At any level, be it atoms, molecules, cells or organisms, there are rich interdependencies among biological components. Machine learning approaches in this domain usually involves analyzing interdependency structures encoded in graphs and related formalisms. In this report, we review our work in developing new Machine Learning methods for these applications with improved performances in comparison with state-of-the-art methods. We show how the networks among biological components can be used to predict properties.
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Acknowledgments
The work is partially supported by Japan MEXT Kakenhi 18K11434 and Vingroup Innovation Foundation (VINIF) project code VINIF.2019.DA18. We greatly appreciate anonymous reviewers for their constructive comments.
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Canh Hao Nguyen is currently a senior lecturer of Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan. His research interest is machine learning, with application to biomedical domains.
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Nguyen, C.H. Structured Learning in Biological Domain. J. Syst. Sci. Syst. Eng. 29, 440–453 (2020). https://doi.org/10.1007/s11518-020-5461-5
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DOI: https://doi.org/10.1007/s11518-020-5461-5