Skip to main content
placeholder image

Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

Journal Article


Download full-text (Open Access)

Abstract


  • Cancer is still one of the most life threatening disease and by far it is still difficult to prevent,

    prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis

    can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because

    of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed.

    In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass

    spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization

    algorithm are also used to select optimized features from mass spectrometry data. The learned features are

    further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection

    accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method

    used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also

    cancer diagnosis.

Authors


  •   Zhou, Qingguo (external author)
  •   Yong, Binbin (external author)
  •   Lv, Qingquan (external author)
  •   Shen, Jun
  •   Wang, Xin (external author)

Publication Date


  • 2020

Citation


  • Zhou, Q., Yong, B., Lv, Q., Shen, J. & Wang, X. (2020). Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection. IEEE Access, 8 (1), 45156-45166.

Scopus Eid


  • 2-s2.0-85082016327

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=4816&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3790

Number Of Pages


  • 10

Start Page


  • 45156

End Page


  • 45166

Volume


  • 8

Issue


  • 1

Place Of Publication


  • United States

Abstract


  • Cancer is still one of the most life threatening disease and by far it is still difficult to prevent,

    prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis

    can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because

    of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed.

    In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass

    spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization

    algorithm are also used to select optimized features from mass spectrometry data. The learned features are

    further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection

    accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method

    used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also

    cancer diagnosis.

Authors


  •   Zhou, Qingguo (external author)
  •   Yong, Binbin (external author)
  •   Lv, Qingquan (external author)
  •   Shen, Jun
  •   Wang, Xin (external author)

Publication Date


  • 2020

Citation


  • Zhou, Q., Yong, B., Lv, Q., Shen, J. & Wang, X. (2020). Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection. IEEE Access, 8 (1), 45156-45166.

Scopus Eid


  • 2-s2.0-85082016327

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=4816&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3790

Number Of Pages


  • 10

Start Page


  • 45156

End Page


  • 45166

Volume


  • 8

Issue


  • 1

Place Of Publication


  • United States