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Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification

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@article{IJASEIT1588,
   author = {Choon Sen Seah and Shahreen Kasim and Mohd Saberi Mohamad},
   title = {Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {1},
   year = {2017},
   pages = {176--182},
   keywords = {directed random walk algorithm; group specific tuning parameter; cancer classification},
   abstract = {Accuracy of cancerous gene classification is a central challenge in clinical cancer research. Microarray-based gene biomarkers have proved the performance and its ability over traditional clinical parameters. However, gene biomarkers of an individual are less robustness due to litter reproducibility between different cohorts of patients. Several methods incorporating pathway information such as directed random walk have been proposed to infer the pathway activity. This paper discusses the implementation of group specific tuning parameter in directed random walk algorithm. In this experiment, gene expression data and pathway data are used as input data. Throughout this experiment, more significant pathway activities can be identified which increases the accuracy of cancer classification. The lung cancer gene is used as the experimental dataset, with which, the sDRW is used in determining significant pathways. More risk-active pathways are identified throughout this experiment.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1588},
   doi = {10.18517/ijaseit.7.1.1588}
}

EndNote

%A Seah, Choon Sen
%A Kasim, Shahreen
%A Mohamad, Mohd Saberi
%D 2017
%T Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification
%B 2017
%9 directed random walk algorithm; group specific tuning parameter; cancer classification
%! Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification
%K directed random walk algorithm; group specific tuning parameter; cancer classification
%X Accuracy of cancerous gene classification is a central challenge in clinical cancer research. Microarray-based gene biomarkers have proved the performance and its ability over traditional clinical parameters. However, gene biomarkers of an individual are less robustness due to litter reproducibility between different cohorts of patients. Several methods incorporating pathway information such as directed random walk have been proposed to infer the pathway activity. This paper discusses the implementation of group specific tuning parameter in directed random walk algorithm. In this experiment, gene expression data and pathway data are used as input data. Throughout this experiment, more significant pathway activities can be identified which increases the accuracy of cancer classification. The lung cancer gene is used as the experimental dataset, with which, the sDRW is used in determining significant pathways. More risk-active pathways are identified throughout this experiment.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1588
%R doi:10.18517/ijaseit.7.1.1588
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 1
%@ 2088-5334

IEEE

Choon Sen Seah,Shahreen Kasim and Mohd Saberi Mohamad,"Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, pp. 176-182, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.1.1588.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Seah, Choon Sen
AU  - Kasim, Shahreen
AU  - Mohamad, Mohd Saberi
PY  - 2017
TI  - Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 1
Y2  - 2017
SP  - 176
EP  - 182
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - directed random walk algorithm; group specific tuning parameter; cancer classification
N2  - Accuracy of cancerous gene classification is a central challenge in clinical cancer research. Microarray-based gene biomarkers have proved the performance and its ability over traditional clinical parameters. However, gene biomarkers of an individual are less robustness due to litter reproducibility between different cohorts of patients. Several methods incorporating pathway information such as directed random walk have been proposed to infer the pathway activity. This paper discusses the implementation of group specific tuning parameter in directed random walk algorithm. In this experiment, gene expression data and pathway data are used as input data. Throughout this experiment, more significant pathway activities can be identified which increases the accuracy of cancer classification. The lung cancer gene is used as the experimental dataset, with which, the sDRW is used in determining significant pathways. More risk-active pathways are identified throughout this experiment.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1588
DO  - 10.18517/ijaseit.7.1.1588

RefWorks

RT Journal Article
ID 1588
A1 Seah, Choon Sen
A1 Kasim, Shahreen
A1 Mohamad, Mohd Saberi
T1 Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 1
YR 2017
SP 176
OP 182
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 directed random walk algorithm; group specific tuning parameter; cancer classification
AB Accuracy of cancerous gene classification is a central challenge in clinical cancer research. Microarray-based gene biomarkers have proved the performance and its ability over traditional clinical parameters. However, gene biomarkers of an individual are less robustness due to litter reproducibility between different cohorts of patients. Several methods incorporating pathway information such as directed random walk have been proposed to infer the pathway activity. This paper discusses the implementation of group specific tuning parameter in directed random walk algorithm. In this experiment, gene expression data and pathway data are used as input data. Throughout this experiment, more significant pathway activities can be identified which increases the accuracy of cancer classification. The lung cancer gene is used as the experimental dataset, with which, the sDRW is used in determining significant pathways. More risk-active pathways are identified throughout this experiment.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1588
DO  - 10.18517/ijaseit.7.1.1588