Fast metabolite identification with Input Output Kernel Regression.

ABSTRACT:

Motivation

An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space.

Results

We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and

SUBMITTER: Brouard C 

PROVIDER: S-EPMC4908330 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature