This study focused on modeling interactions between these features in order to infer their potential biological significance in sarcoma cancers. It is difficult to objectively compare the biological relevance of individual genes identified between studies, as complex biology cannot be quantified using numerical analyses. Published literature has argued that the biological relevance between features can be achieved using correlation analysis and a pre-defined baseline value of the parameter which is known to be biologically meaningful. However, this information alone is not sufficient for comparing which feature has more biological meaning than another, as the full biological function of the features and the biochemical reaction mechanisms underlying regulatory interactions between features cannot be fully known without conducting a thorough assessment in clinical materials relevant to the disease status in order to describe behaviors of these features in vivo. This clinical assessment and validation is not within the remit of this paper and instead of looking for more biological meaningful features, this paper reports a complementary set of genes to those reported by Khan et al. For the sake of conciseness of the interpretation of the biological relevance on the selected genes in the dataset, the biological functionality of the genes associating with 2 types of sarcomas, and interactions that are of potential relevance on the basis of plausible biological explanations and the correlation analysis of the genes were studied in this paper. The concept of the interactome network map in which the internal organization and functional regulation of cells can be presented using network/graph theory was initially set out by Baraba��si and Oltvai. In the network map, a Catharanthine sulfate single gene is symbolized by a node, and the link between genes is known as an edge, which can be presented with an arrow to indicate the Nomifensine Maleate direction of the link from a source node to a target node. Interactome network maps have been used to demonstrate interactions between biological components. These originally utilized off-the-shelf or publicly available modeling tools to analyze associations between biological molecules, but later used customized data mining tools to comprehensively model the interaction between biological molecules.