Overview


In our laboratory we develop and apply computational methods for studying the function of genes and proteins by integrating sequence, structure and gene expression data. Generally we are interested in identifying the network of molecular interactions involved in the gene expression pathway, specifically at the level of post-transcription regulation. Our main goal is to understand the underlying principles of molecular recognition and regulation (involved in the gene expression pathway) and predict potential drug targets. Currently there are four major projects ongoing in our laboratory:
  1. Developing machine learning approach for studying protein-RNA recognition.
  2. Studying splicing regulation towards deriving splicing regulatory networks.
  3. Predicting drug binding sites on the ribosome / studying the mechanism of ribosome-drug recognition.
  4. Applying machine learning approaches for predicting and classifying protein function from metagenomics data.