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Bayesian Hypotheses Testing in Raman Spectroscopy

Bayesian Hypotheses Testing in Raman Spectroscopy

This video was recorded at Workshop on Learning and Inference in Computational and Systems Biology (LICSB), London 2009. Surface enhanced resonance Raman spectroscopy (SERRS) can be used to detect a wide range of biochemical species by employing a specific set of nanoparticle probes. New data obtained using this technology will significantly improve our abilities to understand biological systems by enabling high throughput measurements of protein concentrations. Analysis of spectra produced by SERRS is often done manually, and a solid statistical approach to interpreting such results is very important to draw valid conclusions. We model data obtained using SERRS using Gaussian Processes. This modelling approach enables computing marginal likelihoods over different covariance functions of GPs, and therefore consistent hypotheses testing can be performed. We investigate several important problems in analytical biochemistry: • Whether the spectroscopic response of analytes changes in time, or the observed variations can be explained by measurement errors. • Is it possible to measure the differences in concentrations of an analyte given practical variability of the measurement. • What are the most informative frequency bands to measure the concentration of a given protein with high confidence. We, additionally, develop a calibration procedure based on GP regression of the spectroscopic data using Markov Chain Monte Carlo to marginalise over the hyper-parameters of the covariance function.

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