Non-linear mapping for exploratory data analysis in functional genomics

Background: Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisationdriven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated Results: Publicly available expression data originating from leukaemia, round blue-cell tumours and Parkinson disease studies were analysed. The method distinguished relevant clusters and critical analysis areas. The system does not require assumptions about the inherent class structure of the data, its mapping process is controlled by only one parameter and the resulting t...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Francisco Azuaje, Haiying Wang, Alban Chesneau
• Keywords: BMCBI 2005, Functional Genomics Data, Non-linear Mapping, Relaxation Method