Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive

Background: Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis. Results: Previously, we developed the projective adaptive resonance theory (PART) filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS) patients for the extraction of subtypespecific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest sh...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Hiro Takahashi, Takeshi Nemoto, Teruhiko Yoshida, Hiroyuki Honda, Tadashi Hasegawa
• Keywords: BMCBI 2006, Marker Genes, PART Filtering Method, Prognostic Marker Genes