Mining method for cancer and pre-cancer detection caused by mutant codon 248 in TP53

Abstract

Process of prediction has a substantial function in detecting and efficient protection of cancer. The tumor suppressor P53 is approximately near 50% of all human beings tumors due to the mutations which is appear in the TP53 gene to the cells within updated UMD TP53 Mutation Database Oct. 2017 [1], it is so difficult working with prime data (in excel) to predict and diagnosis cancers. In this research a functional model of mining approach and Artificial Neural Network which is proposed to predict cancer and pre-cancer caused by specific codon mutation (each codon has hundreds mutations cause cancers) of tumor protein P53, and applied this approach on mutability of hotspot codon 248 (exon 7), CGG. The Quick Propagation mechanism has been used for training and testing the Neural Network structure to determine the accuracy of the proposed architecture. This research procedure demonstrates that Neural Network based prediction of Cancer and Premalignant Disease (pre-cancer) of mutated codon 248 and manifests perfect performance in the prognosis of the mutation situation to pre-cancer or cancer in general. Using of data mining preprocessing steps and pattern extraction to construct the prediction model by selecting (8) out of (132) new TP53 gene database fields in order to classify the cases to the target class pathology (Cancer, Pre-cancer) using these fields. A high professional Neural Network software simulation (Alyuda NeuroIntellegence) is used to build the classifier and Neural Network, the testing and experimental results from the proposed architecture shows that using Quick Propagation algorithm is very accurate in term of accuracy and minimum error rates showing the results of accuracy (99.97%, 100%, 99.85%) for (Train, Validation and Test) phases respectively with error rate of (0.0003, 0, 0.0015) for (Train, Validation and Test) phases respectively.