Knowledge representation, integration and management in bioinformatics
Modern biological research is an increasingly data-intensive, high-throughput discipline,
that relies on the automated analysis of an large number of variables in order to progress.
While the constant advances in computer power and database technology make it easier to
manipulate the wealth of data being generated, there is a growing need for efficient and
reliable methods to automatically extract new knowledge from it.
I am developing formalisms and software applications to represent and manage large amounts of genomic knowledge and data in a structured, coherent and efficient way. My approach to this problem relies on methods and tools derived from Computer Science, Artificial Intelligence and Knowledge Engineering, with the goal to create interactive environments allowing researchers to freely "navigate" through an ocean of highly interconnected pieces of knowledge and data points, automatically establishing relationships among them, looking for patterns, detecting trends and generating hypotheses.
Bioinformatics tools for biomedical research
In my previous position at the Children's Hospital Informatics Program I developed SNPper, a web-based, integrated database of human genomic information centered around Single-Nucleotide Polymorphisms (SNPs). SNPper integrates data from multiple public resources (dbSNP, the Human Genome sequence, NCBI Entrez, SWISS-Prot, etc) to provide a seamless, consistent and rich view on the human genome. It is currently being used by thousands of researchers and laboratories around the world to support SNP-based association studies. I was also one of the developers of MAPPER, an innovative software tool to detect Transcription Factor Binding Sites (TFBSs) in genomic sequences, and of START, a tool to analyze the results of SACO experiments. Other problems I have worked on include the management and analysis of gene expression microarray data, of tissue microarray data, and of fluorescence microscopy images.
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