This research is focusing at the development of novel information methods and systems based on personalised modelling for genomic data analysis and biomedical applications. It has presented a novel personalised modelling framework and system for analysing the data from different sources. The main idea of personalised modelling is based on the assumption that every data sample has its unique pattern only being represented by a certain number of similar samples with a small set of important features. The proposed personalised modelling system is an integrated computational system that combines different information processing techniques, applied at different stages of the data analysis, e.g. feature selection, classification, outcome prediction, personalised profiling and visualisation, etc. This study is a feasibility analysis for personalised modelling on different sources of data, such as gene expression data, proteomic data and SNPs data. The developed algorithms and models are generic which can be potentially incorporated into a variety of applications for data analysis with certain constraints, such as financial risk analysis, time series data prediction, etc.