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Network-based Prediction of Cancer under Genetic Storm.

Ay A, Gong D, Kahveci T - Cancer Inform (2014)

Bottom Line: Here we present a new network-based supervised classification technique, namely the NBC method.We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50-300 genes selected by five feature selection methods.Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Colgate University, Hamilton, NY, USA. ; Department of Biology, Colgate University, Hamilton, NY, USA.

ABSTRACT
Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50-300 genes selected by five feature selection methods. Our results on five large cancer datasets demonstrate that NBC method outperforms traditional classification techniques. Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy. Finally, in-depth analysis of the correlation-based co-expression networks chosen by our network-based classifier in different cancer classes shows that there are drastic changes in the network models of different cancer types.

No MeSH data available.


Related in: MedlinePlus

Network degree distributions of the networks in different cancer classes. The degree distributions of the networks are shown for the NBC method for NCI60 (A) and Leukemia (B) datasets. In each graph, x-axis represents the degree and y-axis represents the frequency.
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f4-cin-suppl.3-2014-015: Network degree distributions of the networks in different cancer classes. The degree distributions of the networks are shown for the NBC method for NCI60 (A) and Leukemia (B) datasets. In each graph, x-axis represents the degree and y-axis represents the frequency.

Mentions: In this cancer dataset, all of the correlation-based co-expression networks formed in different cancer classes show scale-free behavior. However, the frequency of isolated genes and the highest degree in the networks show slight variations in different cancer classes. While more than half of the genes in the networks formed by the NBC method for five classes (classes 3, 4, 6, 7, and 8) is isolated (Fig. 4A), in the remaining three classes (classes 1, 2, and 5) the frequency of the genes that are isolated is 50% or slightly less than 50%. Similarly, while the maximum degree in the classes 3, 4, and 7 ranges between 3 and 4, in classes 1, 5, 6, and 8, it ranges between 5 and 7. Class 2 shows the highest degree (10) in this cancer type.


Network-based Prediction of Cancer under Genetic Storm.

Ay A, Gong D, Kahveci T - Cancer Inform (2014)

Network degree distributions of the networks in different cancer classes. The degree distributions of the networks are shown for the NBC method for NCI60 (A) and Leukemia (B) datasets. In each graph, x-axis represents the degree and y-axis represents the frequency.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4214593&req=5

f4-cin-suppl.3-2014-015: Network degree distributions of the networks in different cancer classes. The degree distributions of the networks are shown for the NBC method for NCI60 (A) and Leukemia (B) datasets. In each graph, x-axis represents the degree and y-axis represents the frequency.
Mentions: In this cancer dataset, all of the correlation-based co-expression networks formed in different cancer classes show scale-free behavior. However, the frequency of isolated genes and the highest degree in the networks show slight variations in different cancer classes. While more than half of the genes in the networks formed by the NBC method for five classes (classes 3, 4, 6, 7, and 8) is isolated (Fig. 4A), in the remaining three classes (classes 1, 2, and 5) the frequency of the genes that are isolated is 50% or slightly less than 50%. Similarly, while the maximum degree in the classes 3, 4, and 7 ranges between 3 and 4, in classes 1, 5, 6, and 8, it ranges between 5 and 7. Class 2 shows the highest degree (10) in this cancer type.

Bottom Line: Here we present a new network-based supervised classification technique, namely the NBC method.We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50-300 genes selected by five feature selection methods.Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Colgate University, Hamilton, NY, USA. ; Department of Biology, Colgate University, Hamilton, NY, USA.

ABSTRACT
Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50-300 genes selected by five feature selection methods. Our results on five large cancer datasets demonstrate that NBC method outperforms traditional classification techniques. Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy. Finally, in-depth analysis of the correlation-based co-expression networks chosen by our network-based classifier in different cancer classes shows that there are drastic changes in the network models of different cancer types.

No MeSH data available.


Related in: MedlinePlus