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-dri...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.展开更多
基金The work is partially supported by Japan MEXT Kakenhi 18K11434 and Vingroup In-novation Foundation(VINIF)project code VINIF.2019.DA18.
文摘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.
