UNIVERSITY OF MALTA
LIFE SCIENCE RESEARCH SEMINARS
Web: http://www.um.edu.mt/events/scisem/
Email: scisem@um.edu.mt
Abstract form
Title: Measures to characterize variable importance in classification
of microarray datasets
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Clint Mizzi
99905912
mizziclint@gmail.com
7 November 2011
Abstract
In recent years the field of molecular biology has evolved rapidly thanks to a number
of significant projects including the human genome projects. This research was only
possible with the help of computational techniques, which have opened the doors to
a new interdisciplinary field named bioinformatics. This specialisation is well
integrated in molecular biology and is used in solving problems related to genome
analysis, functional prediction and protein prediction.
Classification algorithms have been used in different bioinformatics fields including
gene expression microarray datasets. One of the challenges regarding this area is
identifying feature importance by, determining the most relevant attributes in a
classifier. The aim of this project is to study the use of feature selection methods as
a solution to identify the most important genes. Different cancer datasets were used
to compare and contrast different classification and feature selection algorithms,
including the new random jungle methodology. This project used Classification and
Feature Selection techniques to generate gene expression networks. The aim of this
report is to present a novel algorithm, which improves on GENIE3 by using
Correlation Feature Selection Subset Evaluator to generate gene expression
networks. The networks created were analysed for biological meaning.