Welcome

Clinical Bioinformatics Area

 

The Clinical Bioinformatics Area from the Fundación Progreso y Salud (FPS) has been conceived as a fundamental piece of the Personalized Medicine plan from the Andalusian community, with the mission of facilitating and providing the tools for the inclusion of the genomic data of the patient in the electronical health record.

This Area has the dual aim of developing innovative algorithms and methods for the analysis of genomic of patients, combined with the production of high quality software specifically designed to be used by clinician end users, all this with a strong translational orientation. The ultimate objective of the Area is to bring to the clinician complex algorithms for the management of complex genomics data in a transparent way for them, which ultimately foster the adoption of innovative technologies in the current clinical practice. 


 

Traslational Bioinformatics

 

Our research focuses on the development and use of translational and clinical boinformatics approaches to identify and develop novel diagnostic, prognostic and therapeutic approaches for human disease by integrating molecular and clinical data. We are also developing and evaluating methods to facilitate the use of genomic sequencing data into clinical practice.

 

 

Systems Medicine

 

Genes operate within an intricate network of interactions that we have only recently started to envisage.  Many higher-order levels of interaction are continuously being discovered. In this scenario we are interested in developing methods and tools which can help to understand large-scale experiments from a systems biology perspective.

 

 

Computational Biology

 
We are focused on the developement of advanced computing solutions to solve issues in genomic data analysis. We are intereseted in developing algorithms, databases and tools for the analysis of genomic data that enable researchers to understand what biological processes or variants are involved in different phenotypes or diseases. Our main lines of reasearch cover among others genomic variant analysis, machine learning, NGS analysis or cloud-based solutions for Big Data.