The Andalusian Platform for Computational Medicine one of the research platforms of the Fundación Progreso y Salud (FPS), has been conceived as a fundamental piece of the Personalized Medicine plan of 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 data 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.
Translational Bioinformatics
Personalized medicine is an emerging medical discipline that involves the use of genomic information from an individual as part of its medical care. It has the potential to tailor therapy with the best response and highest safety margin to ensure better patient care. To achieve this, an effective integration of clinical and genomic data is mandatory, as well as a comprehensive analysis of the increasing amount of genomic data.
In this context, we apply cutting-edge bioinformatics methods in order to offer to patients an accurate diagnosis and personalized treatment options.
Software for clinical genomics
We are focused on the development of advanced software solutions to solve issues in genomic data analysis. We are interested in developing databases and tools to facilitate the use of genomic sequencing data into clinical practice.
Our research focuses on the development and use of translational and clinical bioinformatics approaches to identify and develop novel diagnostic, prognostic and therapeutic approaches for human disease by integrating molecular and clinical data.
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 development of advanced computing solutions to solve issues in genomic data analysis. We are interested 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.