Ensembling of EGFR Mutations’ based Artificial Neural Networks for Improved Diagnosis of Non-Small Cell Lung Cancer

Adetiba, E. and Ibikunle, Frank A. (2011) Ensembling of EGFR Mutations’ based Artificial Neural Networks for Improved Diagnosis of Non-Small Cell Lung Cancer. International Journal of Computer Applications, 20 (7). pp. 39-47. ISSN 0975-8887

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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.

Item Type:	Article
Uncontrolled Keywords:	ANN, EGFR, GNEN, NSCLC, LM, SimMicrodel,
Subjects:	R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	04 Dec 2017 14:57
Last Modified:	04 Dec 2017 14:57
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/9767

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