The Sysbiobig group research activity is centered in the development and application of advanced modeling, data mining and machine learning methods for high-throughput biological data analysis in the field of Bioinformatics and Systems Biology.

In particular, the Sysbiobig group has developed/applied different advanced data mining and machine learning methods for robust biomarker discovery, predictive modeling and clustering from microarrays and next generation sequencing (NGS) data. These methods include the prediction of biological annotation and its integration in the learning phase.

The group has also a great expertise in the development/application of differential equation based models, Boolean and Bayesian Networks and optimization techniques.

These methodologies have been exploited for deterministic and stochastic modelling of transcriptional networks and signaling pathways, reverse engineering of transcriptional networks and miRNA-mRNA regulatory motifs, and integration of genetic phenotypic and environmental risk factors via Systems Genetics approaches.

Application to high-throughput data:
In the field of RNA-seq and microarray expression data, the Sysbiobig group has been working on the preprocessing and analysis of static and dynamic expression data.

The Sysbiobig group has also expertise in the preprocessing of SNP arrays and exome sequencing data and multivariate analysis of genetic variations. We recently developed a method for compression and fast retrieval of genetic variants in genome wide association studies.

In the field of Metagenomics, the Sysbiobig group has been working on optimal primer design, pre-processing and analysis of 16S sequencing data and reverse engineering of microbial networks.

In the field of Proteomics the group has developed methods for the preprocessing and quantification of mass spectrometry data and for the modeling of protein turnover from SILAC data.

The group has also collaborated in the development and application of computational methods for genome reconstruction.