The algorithm Clinical Data Partitioning, (CliDaPa) uses histological and clinical data and pharmacological treatments to partition patients by means of a clinical tree representation for a particular disease, used to cluster patients according to similar behaviour. It then uses data mining techniques to analyse each patient partition with the associated genetic information.

The method has been successfully validated on breast and lung cancer and a brain tumour (medulloblastomas).

The Madrid’s Supercomputing and Visualization Centre (CeSViMa), one of the country’s major intensive computing infrastructures, was used to execute CliDaPa.

Compared with different studies reported in the scientific literature and traditional analysis techniques, the results show that CliDaPa offers a significant improvement on earlier results.

The research was developed as part of (right) Santiago González Tortosa’s PhD thesis, co-supervised by Victor Robles, of the Universidad Politécnica de Madrid’s Facultad de Informática, and Fazel Famili, of the National Research Council of Canada.

New approach and promising algorithm
CliDaPa is a different method of DNA microarray analysis that aims to generate a model representing different patient behaviours (gathered from clinical data).

These behaviours will then be examined separately and specifically by means of data mining. New learning methods are also proposed in the course of the research.

A prominent biomedical application within the Cajal Blue Brain project is the comparative study of clinical data with factors sourced from complementary tests looking into the diagnosis and evolution of neurodegenerative diseases.

The research team is continuing research in the oncology domain in partnership with the Hospital de la Paz, as well as exploring new alternatives for pharmacogenomics data representation through the use of visualization, applying novel virtual reality techniques.

The goal is to give medical experts advice on behaviour of a specific disease based on different patient profiles. The use of this visual technique, combined with an expert’s previous experience, will help to diagnose and treat a disease more effectively and faster than current techniques.