Questions regarding the advantage of these techniques and the relevance of the selected biomarkers to biological processes might explain why very few biomarkers that have been discovered using these approaches have seen clinical applications. Additionally, many of the published studies assumed that biomarker discovery involves merely marker selection and classification. Markers with high statistical power and able to accurately Climbazole predict the disease group are treated as ����best���� markers for clinical use, even though their biological interactivities are not tested in silico. This might explained why clinical trials on these markers have failed. We believe that the identification and validation of biomarkers in silico are equally important in biomarker discovery and are vital for clinical trial development. An in silico simulation of possible biological interaction between the selected candidate markers provides information on the nature of the markers, state of the markers and possible chemical changes on the markers. These information can subsequently improve the success rate in clinical trials and patient care. In short, the biology of phenotype is more than just a list of markers; it is the complex interaction of biological components that defines phenotype. We previously identified a list of high potential marker candidates that are able to differentiate small round blue cell tumors in children. This study builds on previous work which has modeled the interaction between these markers to reveal their potential biological relevance in child sarcoma cancers using a bespoke artificial neural network based interactive algorithm. The sarcoma groups in the SRBCTs dataset reported by Khan et al. were used in this study. The selection of biomarker panels for the SRBCTs dataset was Chlorzoxazone performed using a hybrid genetic algorithm-neural network model, as has been reported in our previous work. The aim of this GANN approach was to identify sets of features that possess significant statistics information and statistical comparison between classification methods based upon gene sets reported by Khan et al. and the GANN model has been elaborated.