Investigating the Role of PTEN and P53 in Autism: Design of A Mutant Information Prediction System (MIPS)

P53 is a tumor suppressor protein that is encoded by the TP53 gene in humans. Certain genetic mutations suppress the normal functioning of P53, causing tumors, and degenerate cell growth, leading to several organ disorders. Research states that PTEN hamartoma tumor syndrome (PHTS), a negative outcome of the germline PTEN mutations, is linked with organ-specific cancers and autism spectrum disorders (ASD). In recent years, deficiency of PTEN has also been found to play a role in altering P53 expressions that triggers/advances autism traits. Application of data mining and supervised machine learning techniques for the precise and early identification of such mutations is one of the challenging tasks in the field of computer science, health care and bioinformatics. We present a novel design of a mutant prediction system by configuring the mutation sites that enable detection of genetic markers from secondary DNA-binding mutation records based on the active/inactive state of the Tumor Protein TP53. This mutant information prediction system is based on the Bayesian probabilities extracted for each mutation of the P53 protein at the different binding sites. We then utilize the rules generated by the Random Forest algorithm to formulate a Mutant Information Predictor System (MIPS) to predict the class of P53 mutant sites. We believe that this system would enable further research in investigating the role of P53 in causing/detecting autism.