Research

We use advanced machine learning approaches (artificial intelligence techniques) to detect cancer as early as possible and help guide treatment accordingly. 

Using novel computational algorithms, we seek to identify new cancer mutations and understand why and where cancer mutations occur. 

Using high-throughput sequencing, we are investigating how the tumor genome (and epigenome) evolves in time and particularly upon drug treatment.

 We use high-throughput experimental approaches and pattern detection techniques to investigate what these genes do and the genomewide epigenomic patterns they mediate.

Additional information coming soon.

We use ChIP-seq, RNA-seq, computational modeling to investigate how genes are regulated in cancer cells and how gene regulation in cancer cells differs from normal cells.

We have developed of innovative computational approaches for analysis of high-throughput experiments (metabolomics, proteomics, high-throughout sequencing, etc) performed on cancer cells.

Additional content coming soon.