报告题目：Metabolite Identification throug Machine Learning

报 告 人：Juho Rousu, Aalto University

报告时间：2014年6月27日10:00

报告地点：理科楼三楼报告厅

报告摘要： Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here I will present our recent work, combining fragmentation tree computation with kernel-based machine learning to predict molecular fingerprints and identify molecular structures.

We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the toppositionof the candidates list.

The work is joint with HuibinShen, Kai Duehrkopand Sebastian Boeckerand will be presented at the ISMB-2014 conference in Boston in July.