Computational & Statistical Methodologies to Identify Biomarkers in Cancer

The advent of post genomic technologies and their application to biomedical problems resulted in a massive increase the complexity of data generated. This has resulted in the need for more refined statistical and computational methods for analysis of this type of data Through comprehensive analysis and modelling approaches, structures or classes in the data can be defined, denovo predictive biomarkers identified and clinical decision support systems developed. This review seeks to provide an overview of some of the computational methodologies that have been used for data-mining complex high throughput data for the identification of new biomarkers or improvement of existing biomarkers. Given the literature is vast within the area of computational algorithms we seek to present commonly used methods.