Proteomic analysis may be useful to investigate disorders of the central nervous system, in order to explore the protein content of cells and of biological fluids in respect of the onset and evolution of diseases. Today, one of the most used proteomic approach includes the separation and visualization of proteins by means of two-dimensional gel electrophoresis (2DE). However the development of fully automatic strategies in extracting information from gel images is still a challenging task. In this paper we applied a computational strategy to the aim of obtaining a compact representation of the original data. This method was applied to an experimental protocol including two different clinical groups of amyotrophic lateral sclerosis (ALS) and peripheral neuropathy patients : 32 2DE maps generated from cerebrospinal fluid (24 pathologic and 8 control subjects) were processed. Quantitative features were extracted to describe each image and dealt with the dimension reduction technique of local tangent space alignment (LTSA). The discovered low-dimensional structure allows to see the gel of the dataset as separable, according to their clinical conditions, showing the informativeness of the adopted descriptors and providing the bases for classification of this kind of samples.
|Title of host publication||8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008|
|Publication status||Published - 2008|
|Event||8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008 - Athens, Greece|
Duration: Oct 8 2008 → Oct 10 2008
|Other||8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008|
|Period||10/8/08 → 10/10/08|
ASJC Scopus subject areas