  There is multiple databases contain datasets of TP53 gene and its tumor
protein 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 diagnosis
mutations patient's then predict the cancer of that patient. This paper
proposed a novel mining of cancer via mutations because there is no mining
method before offers friendly, effective and flexible predict or diagnosis of
cancers via using whole common database of TP53 gene (tumor protein P53) as
dataset and selecting a minimum number of fields in training and testing quick
propagation algorithm which supporting this miming method. Simulating quick
propagation 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 Absolute
Relative Error (0.0057).
