Prediction of drug target sensitivity in cancer cell lines using apache spark

Predicting the drug targets in the base of cancer cell line is one of the hottest issues in cancer treatment. Drug sensitivity describes which drug is perfect for cell line in certain condition or disease. This condition exists due to change in human metabolism. Different techniques are used for cancer treatment such as radiotherapy, hormone therapy, chemotherapy, and surgery. Many statistical methods and machine learning algorithms such as support vector machine, principal component analysis (PCA), logistic regression, simple linear regression, naive Bayes classifier, generalized linear regression, and random forest have been used for drug target prediction. However, these predictors take more time for computation using different tools such as MATLAB and R tool. In this study, different machine learning techniques are applied using Apache Spark to predict drug targets. Apache Spark uses the resilient distributed dataset (RDD) technique for in-memory fast computation and fault tolerance. The obtained results indicate that Spark provides better accuracy in short time when compared with existing tools.