multigenic dissection and prognostic modeling of common diseases: the aim of this project is to develop a comprehensive approach to the identification of the genetic bases of common diseases using bayesian graphical models.

decoding gene expression control using bayesian graphical models: the goal of this project is to develop a theoretical and computational framework for the automated identification of regulatory mechanism of the genome using bayesian graphical models.

comparative analysis and supervised classification of microarray experiments: this project aims at developing bayesian methods the identification of differentially expressed genes and experimental class prediction.

human variation omnibus: the human variation omnibus (HVO) is a public repository of genetic studies aimed at storing, distributing and integrating this information in a standardized medical vocabulary.

efficient haplotype tagging: the aim of this project is to develop methods for the efficient identification of genetic markers able to account of transmission of large regions of the genome to minimize genotyping and understand genetic transmission mechanisms.

multigenic dissection of adult onset asthma: the goal of this project is to identify the genetic basis of the asthma in adults and provide a prognostic model for the risk of asthma development in adult populations.

multigenic dissection of non-syndromic oral clefts: the aim of this project is to identify the genetic bases of oral clefts that do not occurr as part of known syndromes and characterize their interactions with environmental exposures.

multigenic dissection of autism spectrum disorders: the goal of this project is the identification of the multigenic bases of autism through the integration of multiple genetic studies using bayesian graphical models.

feedback regulation and control in cancer cell cycle: the goal of this project is the identification of novel dynamic regulatory mechanisms in the gene expression of the cell cycle to better characterize the biology of cancer development.

cluster analysis of temporal microarray experiments: the aim of this project is to develop a bayesian approach to the analysis of microarray experiments measuring the evolution of a biological system over time.

the musical genome - 4-dimensional display of gene expression and interaction: the aim of this project is to develop methods to turn temporal processes into musical representations and deliver a 4 dimensional display of complex systems, such as interaction networks, gene expression profiles and protein structures.

gopad: the goal of this project is to present relevant information theoretic statistics featuring ontology partitions with Gene Ontology terms of similar specificity to facilitate researchers to analyze information at arbitrary levels.

biofluidome: the aim of this project is to develop a comprehensive framework to identify which biofuilds are most likely to carry peripheral markers of disease or therapeutic response in specific tissues.

massome: this project provides the largest non-redundant network of human protein-protein interactions ever assembled for information that can be used from network architecture about its embedded proteins.

go infocube: this project allows investigators to design and analyze nascent parallelization technologies (e.g. protein microarrays) by projecting constituent proteins/genes onto three application-specific dimensions within Gene Ontology.

the functional landscape of chemotherapy toxicities: the goal of this project is to create an integrated landscape of the relationships between toxicities induced by chemotherapy an develop a prognostic system able to forecast the insurgence of these toxicities.

multigenic dissection of therapy response in asthmatic patients: the goal of this project is to indentify the genetic basis that make some asthma patients more sensitive to treatment than others and to develop a genomic prognostic model for such a response.

in silico pharmacogenomics: the goal of this project is to develop a model of interaction among genes able to reveal the critical point of cancer proliferation and predict the effectiveness of anti-cancer compounds on the basis of the genes they affect.