Advanced machine learning methods for mass spectrometry

2020 Fiscal Year Annual Research Report

• Project/Area Number: 19J14714
• Research Institution: Kyoto University
• Principal Investigator: NGUYEN DaiーHai 京都大学, 薬学研究科, 特別研究員(DC2)
• Project Period (FY): 2019-04-25 – 2021-03-31
• Keywords: machine learning / metabolite / sparse learning / representation learning

Outline of Annual Research Achievements

The results of this research are summarized in four publications. The first publication is a review paper on computational methods for metabolite identification from mass spectra, which appeared in Briefings in Bioinformatics. In the second publication, we proposed machine learning models with the incorporation of peak interactions for predicting molecular fingerprints, which appeared in Bioinformatics (also the proceedings of the 26th International Conference on Intelligent Systems for Molecular Biology (ISMB)). In the third publication, we proposed another machine learning model for predicting representations for metabolites from their chemical structures and spectra, which appeared in Bioinformatics (also the proceedings of the 27th ISMB). All together, we summarized recent advances of machine learning for metabolite identification from mass spectra, including our research results, in a book chapter, which will appear in Creative Complex Systems, Springer 2021.

Research Progress Status

令和2年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

令和2年度が最終年度であるため、記入しない。