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Supervised machine learning model for high dimensional gene data in colon cancer detection

Conference Paper


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Abstract


  • With well-developed methods in gene level data extraction, there are huge amount of gene expression data, including normal composition and abnormal ones. Therefore, mining gene expression data is currently an urgent research question, for detecting a corresponding pattern, such as cancer species, quickly and accurately. Since gene expression data classification problem has been widely studied accompanying with the development of gene technology, by far numerous methods, mainly neural network related, have been deployed in medical data analysis, which is mainly dealing with the high dimension and small quantity. A lot of research has been conducted on clustering approaches, extreme learning machine and so on. They are usuallly applied in a shallow neural network model. Recently deep learning has shown its power and good performance on high dimensional datasets. Unlike current popular deep neural network, we will continue to apply shallow neural network but develop an innovative algorithm for shallow neural network. In the supervised model, we demonstrate a shallow neural network model with a batch of parameters, and narrow its computational process into several positive parts, which process smoothly for a better result and finally achieve an optimal goal. It shows a stable and excellent result comparable to deep neural network. An analysis of the algorithm is also presented in this paper.

Authors


  •   Chen, Huaming
  •   Zhao, Hong (external author)
  •   Shen, Jun
  •   Zhou, Rui (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2015

Citation


  • Chen, H., Zhao, H., Shen, J., Zhou, R. & Zhou, Q. (2015). Supervised machine learning model for high dimensional gene data in colon cancer detection. IEEE BigData Congress (pp. 134-141). New York: IEEE.

Scopus Eid


  • 2-s2.0-84959560169

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6043&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5016

Has Global Citation Frequency


Start Page


  • 134

End Page


  • 141

Place Of Publication


  • New York

Abstract


  • With well-developed methods in gene level data extraction, there are huge amount of gene expression data, including normal composition and abnormal ones. Therefore, mining gene expression data is currently an urgent research question, for detecting a corresponding pattern, such as cancer species, quickly and accurately. Since gene expression data classification problem has been widely studied accompanying with the development of gene technology, by far numerous methods, mainly neural network related, have been deployed in medical data analysis, which is mainly dealing with the high dimension and small quantity. A lot of research has been conducted on clustering approaches, extreme learning machine and so on. They are usuallly applied in a shallow neural network model. Recently deep learning has shown its power and good performance on high dimensional datasets. Unlike current popular deep neural network, we will continue to apply shallow neural network but develop an innovative algorithm for shallow neural network. In the supervised model, we demonstrate a shallow neural network model with a batch of parameters, and narrow its computational process into several positive parts, which process smoothly for a better result and finally achieve an optimal goal. It shows a stable and excellent result comparable to deep neural network. An analysis of the algorithm is also presented in this paper.

Authors


  •   Chen, Huaming
  •   Zhao, Hong (external author)
  •   Shen, Jun
  •   Zhou, Rui (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2015

Citation


  • Chen, H., Zhao, H., Shen, J., Zhou, R. & Zhou, Q. (2015). Supervised machine learning model for high dimensional gene data in colon cancer detection. IEEE BigData Congress (pp. 134-141). New York: IEEE.

Scopus Eid


  • 2-s2.0-84959560169

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6043&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5016

Has Global Citation Frequency


Start Page


  • 134

End Page


  • 141

Place Of Publication


  • New York