  The prediction plays the important role in detecting efficient protection and
therapy of cancer. The prediction of mutations in gene needs a diagnostic and
classification, which is based on the whole database (big dataset), to reach
sufficient accuracy results. Since the tumor suppressor P53 is approximately
about fifty percentage of all human tumors because mutations that occur in the
TP53 gene into the cells. So, this paper is applied on tumor p53, where the
problem is there are several primitive databases (excel database) contain
datasets of TP53 gene with its tumor protein p53, these databases are rich
datasets that cover all mutations and cause diseases (cancers). But these Data
Bases cannot reach to predict and diagnosis cancers, i.e. the big datasets have
not efficient Data Mining method, which can predict, diagnosis the mutation,
and classify the cancer of patient. The goal of this paper to reach a Data
Mining technique, that employs neural network, which bases on the big datasets.
Also, offers friendly predictions, flexible, and effective classified cancers,
in order to overcome the previous techniques drawbacks. This proposed technique
is done by using two approaches, first, bioinformatics techniques by using
BLAST, CLUSTALW, etc, in order to know if there are malignant mutations or not.
The second, data mining by using neural network; it is selected (12) out of
(53) TP53 gene database fields. To clarify, one of these 12 fields (gene
location field) did not exists in TP53 gene database; therefore, it is added to
the database of TP53 gene in training and testing back propagation algorithm,
in order to classify specifically the types of cancers. Feed Forward Back
Propagation supports this Data Mining method with data training rate (1) and
Mean Square Error (MSE) (0.00000000000001). This effective technique allows in
a quick, accurate and easy way to classify the type of cancer.
