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There is multiple databases contain datasets of TP53 gene and its tumorprotein P53 which believed to be involved in over 50% of human cancers cases,these databases are rich as datasets covered all mutations caused diseases(cancers), but they haven't efficient mining method can classify and diagnosismutations patient's then predict the cancer of that patient. This paperproposed a novel mining of cancer via mutations because there is no miningmethod before offers friendly, effective and flexible predict or diagnosis ofcancers via using whole common database of TP53 gene (tumor protein P53) asdataset and selecting a minimum number of fields in training and testing quickpropagation algorithm which supporting this miming method. Simulating quickpropagation network for the train dataset shows results the Correlation(0.9999), R-squared (0.9998) and mean of Absolute Relative Error (0.0029),while the training for the ALL datasets (train, test and validation dataset)have results the Correlation (0.9993), R-squared (0.9987) and mean of AbsoluteRelative Error (0.0057).

Novel Mining of Cancer via Mutation in Tumor Protein P53 using Quick Propagation Network