Classification of Hot Spots using XG Boost and Light GBM Algorithms
Minul Vijayakumar1, Joby George2

1Minul Vijayakumar*, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
2Joby George, Associate Professor and Head of the Department of Computer Science and Engineering of Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.

Manuscript received on May 30, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 722-724 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9459069520/2020©BEIESP | DOI: 10.35940/ijeat.E9459.069520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Protein-Protein Interactions referred as PPIs perform significant role in biological functions like cell metabolism, immune response, signal transduction etc. Hot spots are small fractions of residues in interfaces and provide substantial binding energy in PPIs. Therefore, identification of hot spots is important to discover and analyze molecular medicines and diseases. The current strategy, alanine scanning isn’t pertinent to enormous scope applications since the technique is very costly and tedious. The existing computational methods are poor in classification performance as well as accuracy in prediction. They are concerned with the topological structure and gene expression of hub proteins. The proposed system focuses on hot spots of hub proteins by eliminating redundant as well as highly correlated features using Pearson Correlation Coefficient and Support Vector Machine based feature elimination. Extreme Gradient boosting and LightGBM algorithms are used to ensemble a set of weak classifiers to form a strong classifier. The proposed system shows better accuracy than the existing computational methods. The model can also be used to predict accurate molecular inhibitors for specific PPIs. 
Keywords: Extreme Gradient Boosting (XG Boost), Protein Protein Interaction (PPI), Protein Protein Interaction Network (PPIN, Light GBM.