Abstract
With the flood of publicly available data, it allows scientists to explore and discover new findings. Gene expression is one type of biological data which captures the activity inside the cell. Studying gene expression data may expose the mechanisms of disease development. However, with the limitation of computing resources or knowledge in computer programming, many research groups are unable to effectively utilize the data. For about a decade now, various web-based data analysis tools have been developed to analyze gene expression data. Different tools were implemented by different analytical approaches, often resulting in different outcomes. This study conducts a comparative study of three existing web-based gene expression analysis tools, namely Gene-set Activity Toolbox (GAT), NetworkAnalyst and GEO2R using six publicly available cancer data sets. Results of our case study show that NetworkAnalyst has the best performance followed by GAT and GEO2R, respectively.
W. Engchuan and P. Patumcharoenpol–These authors contributed equally to this work.
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Acknowledgment
The authors would like to thank Ms. Katlin Kreamer-Tonin for proofreading this paper.
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Engchuan, W., Patumcharoenpol, P., Chan, J.H. (2015). Comparative Study of Web-Based Gene Expression Analysis Tools for Biomarkers Identification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_25
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