An Efficient Approach for Cancer Prediction Using Genomic Signal Processing

T.M. Inbamalar(1*), R. Sivakumar(2)

(1) R M K Engineering College affiliated to Anna University, Chennai, India
(2) Department of Electronics and Communication Engineering at R M K Engineering College, India
(*) Corresponding author

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Recent advances in bioinformatics and genomic signal processing have generated much interest due to the integration of theory and methods of signal processing with the global understanding of the functional genomics of organisms. In this paper, we propose a method to predict cancer based on the signal processing of the (Deoxyribo nucleic acid) DNA sequence. In the proposed method, entropy of the DNA sequence is found to predict cancer. For the analysis, we use DNA sequences obtained from National Centre for Biotechnology information (NCBI). Evaluation metrics parameters of Sensitivity, Specificity and Accuracy are found out and compared with the existing methods. From the results, it is clear that our method has attained better evaluation metrics parameters. It gave 86.36% accuracy, 90.9% specificity and 81.81% Sensitivity. When compared to literature, our method produces better results
Copyright © 2014 Praise Worthy Prize - All rights reserved.


Cancer Prediction; DNA Sequences; Entropy; Genomic Signal Processing

Full Text:



D. Anastassiou, Genomic Signal Processing, IEEE Signal Processing Magazine, Vol. 18, No. 4, pp. 8-20, 2001.

P.P. Vaidyanathan and B. J. Yoon, Gene and exon prediction using all pass-based filters, Proceedings of Workshop on Genomic Signal Processing and Statistics (GENSIPS), 2002.

B. Alberts, D. Bray, A. Johnson, J. Lewis, M. Raff, K. Roberts, and P. Walter, Essential Cell Biology (Garland Publishing, New York: 1999).

Edward R Dougherty, Yufei Huang, Seungchan Kim, Xiaodong Cai and Rui Yamaguchi, Genomic Signal Processing, Current Genomics, Vol.10, No.6, pp.364, 2009.

S.Barman, M.Roy, S.Biswas and S.Saha, Prediction of Cancer Cell using Digital Signal Processing, International Journal of Engineering, Vol.3, pp.91-95, 2011.

Qiu, Wang Z.Jane, and K.J. Ray Liu, Genomic Processing of Cancer Classification and Prediction, IEEE Signal Processing Magazine, 2007.

Dougherty Edward R. and Dutta Aniruddha, Genomic Signal Processing: Diagnosis and Therapy, IEEE Signal Processing Magazine, 2005.

Chen Jie and Wong Stephen T.C., Nanotechnology for Genomic signal Processing in cancer Research, IEEE Signal Processing Magazine, 2007.

Tabatabaee-Y., H., Mehrnejad, M., Kazem Shekofteh, S., Cancer detection based on experimental sampling by genetic-fuzzy classification system, (2012) International Review on Computers and Software (IRECOS), 7 (3), pp. 1062-106.

S. Y. Kung, YuhuiLuo and Man-WaiMak, Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios, Journal Of Signal Processing Systems, Vol. 61, No. 1, pp.3-20, 2010.

Akhtar.M, Epps.J, Ambikairajah.E, Signal Processing in Sequence Analysis: Advances in Eukaryotic Gene Prediction, IEEE Journal of Selected Topics in Signal Processing, Vol.2, No.3, pp.310 - 321, 2008.

Sungoor.A and Nebel.J, Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering, IEEE Transactions on Nano Bioscience, Vol.10, No.4, pp.225 - 238, 2011.

Jens K. Habermann, Jana Doering, SampsaHautaniemi, Uwe J. Roblick, Nana K. Bündgen, Daniel Nicorici, Ulrike Kronenwett, ShrutiRathnagiriswaran, Rama K. R. Mettu, Yan Ma, Stefan Krüger, Hans-Peter Bruch, Gert Auer, Nancy L. Guo and Thomas Ried, The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome, International Journal of Cancer, Vol. 124, No. 7, pp. 1552–1564, 2009.

A.Ghosh and S.Barman , Prediction of Prostate Cancer cells based on Principal Component Analysis Technique, International Conference on Computational Intelligence: Modeling Techniques and Applications (Elsevier), pp 37-44 , 2013

Jiarui Ding, et al ‘Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data’, Bioinformatics,Vol. 28 , no. 2, 2012, pp 167–175

K.C. Allen Chan et al, ‘Cancer Genome Scanning in Plasma:Detection of Tumor-Associated Copy Number Aberrations,Single-Nucleotide Variants, and Tumoral Heterogeneity by Massively Parallel Sequencing’, Clinical Chemistry- Molecular Diagnostics and Genetics, No.59:1, 2013, pp 1-14

Ayad Ghany Ismaeel and Anar Auda Ablahad, ‘Novel Method for Mutational Disease Prediction using Bioinformatics Techniques and Backpropagation Algorithm’, Engineering Science and Technology: An International Journal, Vol.3, No.1, 2013, pp 150-156.

Piyushi Singh, James Rohit Runda, Devanshu Umredkar & Rahul Shrivatava, ‘Analysis of Fractal Nature of Cancer Genes’, Octa Journal of Biosciences, Vol. 1(1), 2013 , pp 1-7

National Centre for Biotechnology Information (NCBI). [Online]. Available:

Wen Zhu, Nancy Zeng, Ning Wang, Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations, Proceedings of the SAS Conference, pp.9, 2010.


  • There are currently no refbacks.

Please send any questions about this web site to
Copyright © 2005-2017 Praise Worthy Prize