University of Birmingham > Talks@bham > Medical Imaging Research Seminars >  Hierarchical Learning for the Analysis of Mass Spectrometry Data

Hierarchical Learning for the Analysis of Mass Spectrometry Data

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Mass Spectrometry is a technique used to identify the chemical composition of a sample and has been widely used in the biosciences to analyse biological samples. The focus of this talk will be on Mass Spectrometry Imaging (MSI) & Tandem Mass Spectrometry (MS/MS) used in proteomics. The main challenges with Mass Spectrometry data are the complexity and the amount of data generated. Both MSI and MS/MS generate large amounts of complex data that can not be analysed by hand. Current approaches to the analysis of MSI data use standard algorithms such as clustering and component analysis. We propose using an approach that takes advantage of the hierarchical nature of the data to produce an easy to interpret representation of the data based on hierarchical compositions from computer vision. We also present an approach to MS/MS data analysis using some of the recent developments in Deep Learning.

This talk is part of the Medical Imaging Research Seminars series.

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