Abstract
In the presented work, we propose two approaches to study the similarity between proteomic maps. First, we introduced similarity index as a robust parameter to express the degree of similarity among proteomic maps. Second, we treated the proteomic maps with self-organizing map (SOM) technique taking the abundances of spots as input variables. The study was performed on a set of reported proteomic maps, which were experimentally derived from mouse liver after the animals were treated with peroxisome proliferators. (Entire study included five peroxisome proliferators, control, and a non-peroxisome proliferating compound.) In original paper, the authors analyzed the data with the principal component method. Later other authors reported the study on the same data set using different numerical representations of proteomic maps. The presented results are in agreement with reported conclusions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
