Publications by Elisa Ficarra

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologies.

Nowadays the need for powerful hardware architectures, which allow for high throughput data analysis and calculus, is fundamental especially for biological applications. We have been focused on utilizing the Graphic Processing Unit (GPU) architectures of NVIDIA for accelerating a lipid bilayer simulation tool for biomembranes. ©2010 IEEE.

Gene regulation is one of the most important processes in the molecular biology, in the last years the microRNA molecule, one of the non-coding RNAs involved in the process, has been the focus of attention for several studies. The computational research on this area has gained a notable importance, considering the low amount of experimental information available and the lack of understanding of the microRNA binding mechanism. This article deals with the microRNA-target prediction and presents an innovative method for it. First it generates a set of promising binding sites for a given microRNA using a Genetic Algorithm, at the same time a set of target genes is selected based on the biological process under study. Secondly the set of promising binding sites is mapped into the selected set of target genes, in order to provide real binding sites and finally the resulting targets are filtered according to a biological or structural property. The objectives are to provide a flexible method that is capable of incorporating easily new knowledge, is independent of availability of the experimental information and is able to give hints on the research towards new characteristics among the microRNA binding sites such as motifs. The results present some of this novel properties and present a comparison with the most frequently used methods in the field. © 2010 IEEE.