Topologically significant directed random walk with applied walker network in cancer environment.

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ID: 55614
2019
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Abstract
Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.
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seah2019topologicallypakistan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Seah, Choon Sen;Kasim, Shahreen;Saedudin, Rd Rohmat;Md Fudzee, Mohd Farhan;Mohamad, Mohd Saberi;Hassan, Rohayanti;Ismail, Mohd Arfian;
Journal pakistan journal of pharmaceutical sciences
Year 2019
DOI
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URL URL not found
Keywords
cluster analysis clinical data cluster optimization cluster stability cluster tendency cluster validation stratification

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