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

Deeman Yousif Mahmood, Ayad Ghany Ismaeel, Abbas Hassan Taqi


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.

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References , “UMD TP53 Mutation Database” Oct, 2017.

Zahraa N. Shahweli, Ban N. Dhannoon, Rehab S. Ramadhan (2017), "In Silico Molecular Classification of Breast and Prostate Cancers using Back Propagation Neural Network", Cancer Biology, 7(3).

Evan H. Baugh, Hua Ke, Arnold J. Levine, Richard A. Bonneau and Chang S. Chan (2018), "Why are there hotspot mutations in the TP53 gene in human cancers", Cell Death and Differentiation 25, 154–160.

Ayad Ghany Ismaeel (2013), "New Approach for Prediction Pre-cancer via Detecting Mutated in Tumor Protein P53", International Journal of Scientific & Engineering Research, Volume 4, Issue 10.

Jiandong Chen (2016),"The Cell-Cycle Arrest and Apoptotic Functions of p53 in Tumor Initiation and Progression", Cold Spring Harb Perspect Med 2016.

Praveen K. Jaiswal, Apul Goel, Rama D. Mittal (2011), "Association of p53 codon 248 (exon7) with urinary bladder cancer risk in the North Indian population", BioScience Trends, 5(5):205-210.

Ayad. Ghany Ismaeel, Raghad. Zuhair Yousif (2015), "Novel Mining of Cancer via Mutation in Tumor Protein P53 using Quick Propagation Network", International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 3, Issue 2.

Zahraa N. Shahweli, Ban N. Dhannoon (2017), "NEURAL NETWORK WITH NEW RELIEF FEATURE SELECTION FOR PREDICTING BREAST CANCER BASED ON TP53 MUTATION", International Research Journal of Computer Science (IRJCS), Issue 12, Volume 4.

Chaurasia V., Pal S. (2014), “A novel approach for breast cancer detection using data mining techniques”, International Journal of Innovative Research in Computer and Communication Engineering, 2(1), 2456-65.

Francesca Pentimalli (2017), "Updates from the TP53 universe", Cell Death and differentiation, doi:10.1038/cdd.2017.190.

Marcilio C. P., Natal, Brazil, Daniel S. A. de Araujo, Natal, Brazil Ivan G. Costa, Rodrigo G. F. Soares, Teresa B. Ludermir, Alexander Schliep (2008). "Comparative study on normalization procedures for cluster analysis of gene expression datasets", 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), DOI: 10.1109/IJCNN.2008.4634191.

D.Shanthi, Dr.G.Sahoo and Dr.N.Saravanan (2009), "Designing an Artificial Neural Network Model for the Prediction of Thrombo-embolic Stroke", International Journals of Biometric and Bioinformatics (IJBB), Volume (3) : Issue (1).

Dongare A., Kharde R. and Amit D. (2012), "Introduction to Artificial Neural Network", International Journal of Engineering and Innovative Technology (IJEIT), Vol. 2, Issue (1).

R Aruna Flarence, Srikanth Bethu, V Sowmya, Kollu Anusha, and B Sankara Babu (2018). "Importance of Supervised Learning in Prediction Analysis", Periodicals of Engineering and Natural Sciences, Vol.6, No.1, 201-214.

Abbas H. Taqi (2015), "LEARNING RATE COMPUTATION FOR THE BACKPROPAGATION ALGORITHM", International Journal of Mathematics and Computer Applications Research (IJMCAR), Vol. 5, Issue 5, 65-72.

Massimo Buscema (1998), "Back Propagation Neural Network", Substance Use & Misuse, 33(2), 233–270.

Clemens A. Brust, Sven Sickert, Marcel Simon, Erik Rodner, and Joachim Denzler (2016), "Evaluation of QuickProp for Learning Deep Neural Networks", arXiv:1606.04333v2. , under the Registration #: B276-2473-3174-Y5B6.

Md. Milon Islam, Hasib Iqbal, Md. Rezwanul Haque, and Md. Kamrul Hasan (2017), "Prediction of Breast Cancer Using Support Vector Machine and K-Nearest Neighbors", 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh.



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Copyright (c) 2019 Deeman Yousif Mahmood

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License