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Abstract:
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An important objective in cell biology is to determine the subcellular location of different proteins. Determining the subcellular location is crucial since the function of proteins in the cell is closely related to their subcellular locations. Identifying the subcellular location of proteins can be accomplished either by using biochemical experiments or by developing computational predictors. Since the former method is both time-consuming and expensive, the computational predictors provide a more advantageous and efficient method of solving the problem. Computational predictors are also ideal in solving the problem of predicting protein subcellular locations since the number of newly discovered proteins have been increasing tremendously as a result of the genome sequencing project. The objective of this research study is to predict the subcellular location of animal and human proteins using computational predictors. The method used for representing proteins in the study is the Pseudo-Amino Acid Composition. This study examines the performance of random forest, AdaBoost, SAMME, and Support Vector Machine in predicting the subcellular location of proteins in animal and human.
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