XGBoost and Network Analysis for Prediction of Proteins Affecting Insulin based on Protein Protein Interactions - Mohammad Hamim Zajuli Al Faroby; Mohammad Isa Irawan; Ni Nyoman Tri Puspaningsih (Universitas Muhammadiyah Malang, 2020)
Protein Interaction Analysis (PPI) can be used to identify proteins that have a supporting function on the main protein, especially in the synthesis process. Insulin is synthesized by proteins that have the same molecular function covering different but mutually supportive roles. To identify this function, the translation of Gene Ontology (GO) gives certain characteristics to each protein. This study purpose to predict proteins that interact with insulin using the centrality method as a feature extractor and extreme gradient boosting as a classification algorithm. Characteristics using the centralized method produces features as a central function of protein. Classification results are measured using measurements, precision, recall and ROC scores. Optimizing the model by finding the right parameters produces an accuracy of and a ROC score of . The prediction model produced by XGBoost has capabilities above the average of other machine learning methods.