Thanks to high-throughput biotechnologies, we are assisting to a massive stacking of genomic, transcriptomic, proteomic and metabolomic data. Molecular biology is nowadays turning into a "data-rich" science. This unprecedented richness of raw data is meaningless until we cannot extract the interesting and useful "needles" of information from the big "haystack" of available data. This allow to us to have at the same time both a "bird’s eye" and a "magnified" overview on a biological sample from a wide range of medically relevant disorders. "The answer is there, blowing in the bulk" (thanks Zimmerman 😃). However, this huge amount of raw data is of little use without groundbreaking bioinformatics and biostatistics methodologies able to disentangle, process, analyze and interpret them.
In my research, I would like to strike a balance between two ways of doing science. On one hand I would like to focus on the design, development and application of novel statistical and machine learning approaches for analyzing heterogeneous and high-dimensional biomedical data. On the other hand, I would like to package such approaches into modular, scalable and user-friendly software tools, in order to make them available to the scientific community at large (have a look at my Software page).
Even if Bioinformatics and Computational Biology are often considered synonymous, I believe they are conceptually separated and it is worth distinguishing them.
When I design new methodologies I am involved in a Computer Science activity. I design algorithms not to solve just a single specific problem, but a class of similar problems in Medicine or Molecular Biology.
When I use my software (or those of others) to answer biomedical questions, I am doing science and I am making inferences in the field of Molecular Biology or Medicine.
Anyway, despite the difference between Bioinformatics and Computational Biology sounds apparently easy and clear, I believe that dealing with complex computational issues without losing sight of the medical/biological point of view, is simultaneously challenging and captivating!!
During my PhD, I focused on the analysis and construction of complex biomolecular networks and on design and implementation of output-structured learning algorithms for gene/protein function prediction and for the discovery of novel associations between human gene and abnormal phenotype. More in general, my research lines can be grouped as follow: