Modeling a Gene Structure Behavior Analysis based on the Correlation Ontology

The ever increasing digitization and advancement in the medical filed provides data especially related to gene structure and computing models gives an opportunity to analyses those data for the more critical classifications and analysis to provide practitioner a better decision-making platform to advice proper treatment. The subtype classification is a challenging task if it is handled only by the computer vision methods, whereas if the low-level relationship is established and structure of the gene profile is understood then the statistical methods are quite useful and effective for the sub-type doses classifications. This paper presents a process of analyzing the gene structure and its correlations among the node behavior analysis by modeling it at the numerical computing platform. Various performance metrics like p-score and t-test is conducted to get the optimal performance factor. The proposed methods can be extended to the further critical computations in advanced models and get the analysis of typical gene profile structure behaviors and used as an effective classifier for the sub-type classifier of the various type of doses sub-cluster. The computational analysis shows significant improvement (50%) in type-1 and type-2 gene expression analysis.