Prostate Cancer Screening for Specific Races Using Bioinformatics and Artificial Intelligence on Genomic Data

Abstract

Prostate cancer is one of a deathly cancer worldwide. The higher incidence and mortality rate shows that it is an urgent call for all of us to fight against it in our own way. This study develops an artificial intelligence system to screening prostate cancer from normal patients in a specific race. Gene expression and its phenotype dataset was downloaded from xenabrowser.net Data preprocessing and filtering based on a particular race, bioinformatics computational analysis to determine the features and machine learning algorithm such as decision tree and random forest are used to develop AI model. All the procedure and analysis was performed using python programming The result show that only White and Black African American that has a proper number of dataset while Asian and American Indian has a very lack dataset.  Differentially expression gene (DEG) analysis was performed to both White and Black African American cancer and normal dataset as a reference. 143 and 1 DEG are found in White and Black African American race respectively. ENSG00000225937.1 (PCA3) is identified as the highest up-regulated gene expression in cancer in both White and Black African American race. The results of DEG analysis then become features to develop Artificial Intelligence (AI) classification system. AI model was developed using decision tree and random forest with GriDSearch parameters optimization and stratified 10-fold cross validation. Both Decision tree and random forest model yield 96% accuracy in training dataset and 93% and 91% accuracy in testing dataset for decision tree and random forest, respectively.