
There are no projects in the garbage can.
Omics technologies and new experimental techniques allow the simultaneous quantification of thousands of biological variables and generate large volumes of data. This amount of information necessitates the use of appropriate analysis techniques to handle and extract knowledge from these massive datasets. Our main research lines focus on developing and applying new computational and statistical methods for the integration and analysis of multi-omics and biomedical data, aiming to achieve a better understanding of the molecular mechanisms associated with complex diseases, as well as advancing their diagnosis and treatment. Specifically, we are interested in developing applications and methodologies in
different contexts:
The growth of omics techniques has led to an explosion in the availability of data in public repositories. With the appropriate methodologies, this data is an invaluable source for generating new knowledge, hypotheses, and predictive models. In our group, we have developed new methods and software based on meta-analysis techniques and data integration, applied to biomarker discovery and functional annotation analysis,among other fields.
We are creating algorithms to establish omics signatures that define new classification models and guide in predicting responses to treatments or prognosis of pathologies, also integrating information from electronic medical records. In this context, we have various collaborations with other expert groups in different pathologies such as autoimmune diseases and cancer.
One of our objectives is also to develop open-source applications that make our methodologies available to the scientific community. These applications are widely used for analyzing biomedical data. The list of developed software is available at https://compbio.ugr.es/tools/.