This is a partial list of possible projects in my group (contact Olivier Elemento for more details on any of these projects)
GENOMIC DATA MINING AND DISPLAY
A PAGE-based framework for systematic interpretation of gene expression studies
Displaying genomic data in the CAVE virtual environment
Clustering large scale heterogeneous genomic datasets using information theory
Explaining gene expression using synergistically informative pathways (extension to PAGE)
Software library for information-theoretic analysis of genomic data
Integrating FIRE, FIRE-pro and PAGE in one tool with GUI (Java or C++)
Measure clustering quality using PAGE and FIRE
Software/wiki to draw/distribute/simulate/etc gene networks
Improving discovery of orthologs using genetic algorithms
DISEASE-ORIENTED COMPUTATIONAL BIOLOGY
Studying the co-expression network of GPCRs, predict drug specificity
Identifying A-to-I editing targets in the brain using data integration
Predicting methylation patterns in lymphoma
Predicting AID targets in B cells using data integration
REGULATORY ELEMENTS AND GENE/PROTEIN REGULATION
Integrating conservation into the FIRE motif discovery process
Understanding the interplay between regulatory elements and nucleosomes
Discovering enhancers using comparative genomics
Discovering boundary elements
Dissecting the structure of core promoters
Discovering motifs involved in splicing using comparative genomics
Predicting the functional consequence of regulatory mutations
Detecting selection in human promoters
Extracting regulatory sequences from the literature using text mining
Discovering protein motifs using network-level conservation
An information-theorerical approach for learning regulatory programs
Finding informative RNA structural elements in 5' and 3'UTRs
Explaining conservation using FIRE (systematic discovery of contextual regulatory sequences)
Reconstructing a cancer-oriented regulatory network using comparative genomics
Studying the link between motif affinity and function
Identifying regulators from motifs and large expression datasets
Revealing motifs underlying chromatin changes and epigenomic modifications
Understanding mRNA and protein abundance (as opposed to gene expression differences)
Discovering motifs with a role in dynamic nucleosome positioning
Learning predictive model of regulation using complete feature integration
An integrative framework for explaining gene expression (learning motifs and rules at the same time)
Evolution of the transcription machinery in vertebrates and mammals and euks
