Modeling a Gene Structure Behavior Analysis based on the Correlation Ontology
Sudha V1, Girijamma H A2
1Sudha V, Assistant Professor, Department of IS&E, RNS Institute of Technology, Bengaluru (Karnataka), India.
2Girijamma H A, Professor, Department of Computer Science & Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 408-413 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10371292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1037.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Biomedical, Gene Structure, Gene Ontology, Clustering Support Vector Machine.
Scope of the Article: Predictive Analysis