An Enhanced Prediction Model for Essential Proteins Prediction for Human Diseases
D. Narmadha1, A. Pravin2
1D. Narmadha, Research Scholar, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2D. Narmadha, Assistant Professor, Karunya Institute of Technology and Science, Coimbatore (Tamil Nadu), India.
3Dr. A.Pravin, Assistant Professor, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1656-1663 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6805048419/19©BEIESP
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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: Proteins play an important role in human biological system. Proteins interact with other molecules such as DNA, RNA and other proteins to perform biological activities. Essential proteins are indispensable for the survival of an organism. The identification of essential proteins is important for finding the disease treatment, develop novel drugs. Numerous topological and machine learning approaches have been introduced in recent past for essential protein prediction but they have not attained promising results. In order to improve the prediction accuracy of essential protein identification the proposed prediction model is constructed by incorporating graph coloring and machine learning approaches. Numerous performance measures namely accuracy, precision, recall and f-measure were employed to predict the performance of the proposed model. After analysis, it is identified that the proposed model produced promising results as compared to state-of art methods.
Keywords: Classification algorithms, Decision tree, Graph Coloring, Protein-protein interaction, Random Forest, SVM
Scope of the Article: Classification