לוח שנה משולב

In my talk I will describe two web-applications for microarray and gene/protein set analysis, ArrayMining.net and PathLoupe. These use ensemble and consensus methods as well as the possibility of modular combinations of different analysis techniques for an integrative view of (microarray-based) gene sets, interlinking
transcriptomics with proteomics data sources. This integrative process uses
tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net Class Discovery Analysis module, and show how this approach can be combined in a modular fashion with a prior gene set analysis.
The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I will show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with PathLoupe, a new web-tool for network topological analysis of gene/protein sets mapped on a comprehensive human protein-protein interaction network. I will discuss results from a PathLoupe analysis of the complete set of genes currently known to be mutated in cancer.