Machine Learning Simulation Model for Prediction and Classification of Subcellular Localization of HIV Apoptosis Proteins by Amino acid Composition

摘要

Protein (or in general, proteome) Analysis Subcellular Localization Prediction is a process (usually through the use of web-based software) of predicting the location- or destination of a protein within the cell using only the protein sequence as its inputs. Proteins are then likened to letters with proper address and stamps to deliver it on the proper destination. Since the proteins should have proper address to ensure its delivery to the proper localization. The destination of various protein sequences is predicted by the subcellular localization prediction servers. Hence a machine learning simulation model is developed to predict and classify HIV apoptosis proteins subcellular localization sites by their amino acid composition. Of the various predictions software's used Eukaryotic Mploc predicts better results mitochondria with accuracy of 99.1304%, Subloc shows better results with mitochondria with accuracy of 90%, and Virus Ploc shows better results with extracellular space with accuracy of 98.889%.