The week's pick
Random Articles
Reseach Article
Ensembling of EGFR Mutations’ based Artificial Neural Networks for Improved Diagnosis of Non-Small Cell Lung Cancer
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 20 - Number 7 |
| Year of Publication: 2011 |
| Authors: Emmanuel Adetiba, Frank A. Ibikunle |
10.5120/2443-3298
|
Emmanuel Adetiba, Frank A. Ibikunle . Ensembling of EGFR Mutations’ based Artificial Neural Networks for Improved Diagnosis of Non-Small Cell Lung Cancer. International Journal of Computer Applications. 20, 7 ( April 2011), 39-47. DOI=10.5120/2443-3298
Abstract
In this research work, we built and ensembled different EGFR microdeletion mutations’ based Artificial Neural Networks(ANNs) for improved diagnosis of Non-Small Cell Lung Cancer(NSCLC). We developed two novel algorithms, namely; Genomic Nucleotide Encoding & Normalization (GNEN) algorithm to encode and normalize the EGFR nucleotides and SimMicrodel algorithm to programmatically simulate microdeletion mutations. Sample patients’ data with microdeletion mutations were extracted from online EGFR mutation databases and the two novel algorithms (implemented in MATLAB) were applied to these data to generate appropriate data sets for training and testing of the networks. The networks after proper training, were combined using minimum error voting ensembling to predict the number of nucleotide deletions in NSCLC patients. Using this ensembling approach, our simulations achieved predictions with minimal error and provides a basis for diagnosing NSCLC patients using genomics based ANN.
References
- Genetic Disease Information Pronto, www.ornl.gov/sci/techresources/Human_Genome/medicine/assist.html
- Genetics of Cancer, www.britannica.com
- Rogerio C. L., Winfield A. B., Carolyn M., Progress in the treatment of lung cancer. CANCER Care Help and Hope.
- Breathnach O.S., Freidlin B., Conley B. 2001. Twenty-two years of phase III trials for patients with advanced non-small-cell lung cancer: Sobering results. Journal of Clinical Oncology 19, 1734-1742.
- Lynch T.J., Bell D.W., Sordella R. 2004. Activating mutations in the epidermal growth factorreceptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl J. Med 350, 2129-39.
- Paez J.G., Janne P.A., Lee J.C., 2004. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304 1497-500.
- Sharma S. V. 2007. Epidermal growth factor receptor mutations in lung cancer. Nature Rev. Cancer 7, 169–181.
- Eunice L. K., Janusz J., Sarah P. T. Epidermal Growth Factor Receptor Kinase Domain Mutations in Esophageal and Pancreatic Adenocarcinomas, www.aacrjournals.org
- Pao W., Miller V.A. 2005. Epidermal growth factor receptor mutations, small-molecule kinase inhibitors, and non-small-cell lung cancer: current knowledge and future directions. Journal of Clinical Oncology, vol. 23, no.11, 2556-68.
- Hansen L.K., Salamon P. 1990. Neural network ensembles. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 10, 993-1001.
- Sollich P., Krog A. 1996. Learning with ensembles. In Advances in Neural Information Processing Systems 8, Cambridge, MA,MIT Press 190-196.
- Naguib R.N.G., Sherbet G.V. 1997. Artificial Neural Networks in Cancer Research. Pathobiology vol. 65, no. 3, 129-139.
- Zhi-Hua Zhou, Yuan Jiand 1990. Medical diagnosis with C4.5 rule preceded by ANN ensemble, Pattern Analysis and Machine Intelligence, IEEE Transactions, vol.12, no.10, 993 - 1001.
- Frenster, J.H. 1990. Neural Networks for Pattern Recognition in Medical Diagnosis. Annual International Conference in the IEEE Engineering in Medicine and Biology Society, vol. 12, no.3, 1423-1424.
- Naguib R.N.G., Robinson M.C., Neal D.E., Hamdy F.C. 1998. Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer:a pilot study. British Joumal of Cancer vol. 78, no. 2, 246-250
- Paulo J. L., Azzam F. G. 2006. The use of artificial neural networks in decision support in cancer;A systematic review.Neural Networks vol. 19, no.4. .
- Chiou Y.S.P., Lure Y.M.F., Ligomenides P.A. 1993. Neural network image analysis and classification in hybrid lung nodule detection (HLND) system, Proceedings of the IEEE-SP Workshop on Neural Networks for Signal Processing ,517-526.
- Penedo M.G., Carreira M.J., Mosquera A., Cabello D. 1998. Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. IEEE Trans. Medical Imaging vol. 17, no. 6, 872-880.
- Zhi-Hua Zhou, Yuan Jiang, Yu-Bin Yang, Shi-Fu Chen. Lung Cancer Cell Identification Based on Artificial Neural Network Ensembles. National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, P.R.China.
- Pao W., Miller V., Zakowski M., Doherty J., 2004. EGF receptor gene mutations are common in lung cancers from "never smokers" and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc. Natl. Acad. Sci. U S A. vol. 101, no. 36 13306-13311.
- MDL EGFR Mutation Database. City of Hope, www.EGFR.com/EGFRMutationDataByMutAug2005.pdf
- E. Adetiba, J.C. Ekeh, V.O. Matthews, S.A. Daramola, M.E.U Eleanya 2011. Estimating an Optimal Backpropagation Algorithm for Training an ANN with the EGFR Exon 19 Nucleotide Sequence: An Electronic Diagnostic Basis for Non-Small Cell Lung Cancer(NSCLC). Journal of Emerging Trends in Engineering and Applied Sciences, vol. 2, no.1, 74-78.
Index Terms
Keywords