Multi-layer perception artificial neural network predictive modelling of genomic and mass spectrometry data in bioinformatics

Lancashire, LJ, 2006. Multi-layer perception artificial neural network predictive modelling of genomic and mass spectrometry data in bioinformatics. PhD, Nottingham Trent University.

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Abstract

The development of proteomic and genomic applications for the research into different diseases has paved the way for the development of novel approaches for the way in which these systems can be investigated. This provides a novel insight into how proteins and genes are being regulated under different conditions. These approaches are self-limited by the volume of data which they produce, with the majority often being noisy and redundant. Therefore these technologies must be coupled with appropriate computational approaches that are capable of identifying components that are the most important in differentiating between disease states of interest. These must be robust enough to cope with data of this size and nature in order to provide an in depth understanding of these complex proteomic and genomic patterns. This in turn will lead to methods for prognosis and diagnosis of diseases such as cancer, by providing an insight into the proteins and genes which are being expressed differentially depending on the current status of the disease. The research contained within this thesis describes the development and validation of multi-layer perceptron Artificial Neural Network based methodologies for variable selection, biomarker identification and predictive modelling of mass spectrometry and genomic data. Many datasets were used from a range of different sources, such as the mass spectrometry analysis of bacterial pathogens, the mass spectrometry analysis of patients suffering from different grades of melanoma, and gene expression analysis of patients with breast cancer. Results showed that robust and reproducible predictive models could be generated, which predicted class to extremely high accuracies (greater than 95 %) for blind datasets. These approaches were enhanced further to allow for the interrogation of biomarkers identified during the course of the analyses with techniques such as response surface analysis and population structure analysis. Response surfaces showed the direction of response of a biomarker of interest, in relation to whether it was being up or down regulated in a given disease outcome under study. As an adjunct population profiling showed the potential for identifying sub-groups of patients which could subsequently be used to identify those at risk of disease spread based upon their genetic profiles. Finally methods for the derivation of gene regulatory networks have been proposed which allows interactions and pathways to be derived to show how the change in expression of one gene causes a resulting change in many others. As such the results from the experimental work performed in this thesis have resulted in novel contributions to the field of bioinformatics.

Item Type:	Thesis
Creators:	Lancashire, L.J.
Date:	2006
ISBN:	9781369316797
Identifiers:	Number Type PQ10183515 Other
Rights:	This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no quotation from the thesis and no information derived from it may be published without the author’s written consent.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	30 Sep 2020 13:34
Last Modified:	13 Sep 2023 13:49
URI:	https://irep.ntu.ac.uk/id/eprint/41032

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