Clinical bioimaging and functional genomics

Relatore
Elisa Ficarra - Dip. di Automatica e Informatica, Politecnico di Torino
Data e ora
martedì 5 luglio 2005 alle ore 17.30 - caffe`, te` & C. ore 17.00
Luogo
Ca' Vignal - Piramide, Piano 0, Sala Verde
Referente
Franco Fummi
Referente esterno
Data pubblicazione
27 giugno 2005
Dipartimento
 

Riassunto

A consistent amount of research in genomics has been done in the last
few years concerning correlation of gene expression to multi-factorial
genetic pathologies. Microarrays are the main instrument exploited so
far to this purpose. Despite this effort, results obtained are strongly
limited by the poor informative content provided by clustering
techniques applied on microarray?s data.

At the same time, in the field of biomedical and molecular imaging, new
techniques have been shown to be effective in extracting clinical and
functional biological information from images of molecules and tissues.
Moreover, the advent of sophisticated light microscope techniques have
made studying dynamic processes in living cells now available. By
observing processes as they happen within the cell, these techniques add
an important extra dimension to the understanding of cell behavior and
function for early disease detection and drug response. In the clinic,
new applications of conventional imaging technologies are likely to play
increasingly important roles, particularly in oncology.

Up to now, these two independent sources of information, namely
microarrays and bioimaging, have never been correlated to enhance gene
expression analysis or to increase the amount of confidence in the
hypothesized gene expression paths.

In the first part of this seminar, examples of bioimaging applications
aimed at extracting genetic information from molecular and tissue images
will be presented.
In the second part a software framework will be proposed that will be
able to put together data coming from microarrays, clinical research and
biological image processing on pathological tissues and to perform a
joint clustering technique in an automated way.





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