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Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features.

Adetiba E, Olugbara OO - ScientificWorldJournal (2015)

Bottom Line: The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers.The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides.The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159.

View Article: PubMed Central - PubMed

Affiliation: ICT and Society Research Group, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa.

ABSTRACT
This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their "nonensemble" variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

No MeSH data available.


Related in: MedlinePlus

(a) Image of the Voss mapped sequences for the first ten EGFR nucleotides. (b) Image of Voss mapped sequences for the first ten KRAS nucleotides (c) Image of Voss mapped sequences for the first ten TP53 nucleotides.
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fig2: (a) Image of the Voss mapped sequences for the first ten EGFR nucleotides. (b) Image of Voss mapped sequences for the first ten KRAS nucleotides (c) Image of Voss mapped sequences for the first ten TP53 nucleotides.

Mentions: Image representations for the sequences in Tables 3, 4, and 5 were also obtained using the appropriate functions in MATLAB R2012a and sample outputs are, respectively, shown in Figures 2(a), 2(b), and 2(c). The visual inspection of the figures shows that the images of each of the biomarkers in this study are unique. Hence, we should be able to seek for their unique feature representation to aid efficient lung cancer prediction using machine learning classifiers such as artificial neural networks and support vector machines.


Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features.

Adetiba E, Olugbara OO - ScientificWorldJournal (2015)

(a) Image of the Voss mapped sequences for the first ten EGFR nucleotides. (b) Image of Voss mapped sequences for the first ten KRAS nucleotides (c) Image of Voss mapped sequences for the first ten TP53 nucleotides.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352926&req=5

fig2: (a) Image of the Voss mapped sequences for the first ten EGFR nucleotides. (b) Image of Voss mapped sequences for the first ten KRAS nucleotides (c) Image of Voss mapped sequences for the first ten TP53 nucleotides.
Mentions: Image representations for the sequences in Tables 3, 4, and 5 were also obtained using the appropriate functions in MATLAB R2012a and sample outputs are, respectively, shown in Figures 2(a), 2(b), and 2(c). The visual inspection of the figures shows that the images of each of the biomarkers in this study are unique. Hence, we should be able to seek for their unique feature representation to aid efficient lung cancer prediction using machine learning classifiers such as artificial neural networks and support vector machines.

Bottom Line: The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers.The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides.The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159.

View Article: PubMed Central - PubMed

Affiliation: ICT and Society Research Group, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa.

ABSTRACT
This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their "nonensemble" variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

No MeSH data available.


Related in: MedlinePlus