Application of genetic programming in analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer
Poster Oct 29, 2007
Nodal involvement in bladder cancer is an independent indicator of prognosis. This study employed an iterative machine learning process called genetic programming on quantitative expression values of 70 genes to classify primary urothelial carcinoma samples into those associated with or without nodal metastasis. The generated rules showed a strong predilection for ICAM1, MAP2K6 and KDR resulting in gene expression motifs that cumulatively suggested a pattern ICAM1>MAP2K6>KDR for node positive cases.
Despite the developments in conventional PCR, the complexity of multiplex Real Time PCR is still limited due to the lack of sufficient detection channels. To achieve high-end multiplexing capacity on standard Real Time PCR machines, Anapa Biotech has developed the MeltPlex® technology (see box on right).READ MORE