Analysis of SELDI-TOF-MS using ε-support vector regression for ovarian cancer identification

Isye Arieshanti; Yudhi Purwananto

doi:10.1007/978-3-319-02913-9_53

Analysis of SELDI-TOF-MS using ε-support vector regression for ovarian cancer identification

Isye Arieshanti
, Yudhi Purwananto

Laboratory of Algorithms and Programming

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The analysis of protein expression profile using SELDI-TOF-MS can assist early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle these limitations, the ε-Support Vector Regression model to identify ovarian cancer is proposed. We can show that the performance of the model to discriminate the protein expression profile with cancer disease from the normal ones can reach accuracy 99%, specificity 99% and sensitivity 100%. This result shows that the model is promising for SELDI-TOFMS analysis in Ovarian Cancer identification.

Original language	English
Title of host publication	The 15th International Conference on Biomedical Engineering, ICBME 2013
Editors	James Goh
Publisher	Springer Verlag
Pages	207-210
Number of pages	4
ISBN (Electronic)	9783319029122
DOIs	https://doi.org/10.1007/978-3-319-02913-9_53
Publication status	Published - 2014
Event	15th International Conference on Biomedical Engineering, ICBME 2013 - Singapore, Singapore Duration: 4 Dec 2013 → 7 Dec 2013

Publication series

Name	IFMBE Proceedings
Volume	43
ISSN (Print)	1680-0737

Conference

Conference	15th International Conference on Biomedical Engineering, ICBME 2013
Country/Territory	Singapore
City	Singapore
Period	4/12/13 → 7/12/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Ovarian Cancer
SELDI-TOF-MS
Support Vector Regression

Access to Document

10.1007/978-3-319-02913-9_53

Cite this

@inproceedings{a36dcead2ad04c158d1003e3ff969da2,

title = "Analysis of SELDI-TOF-MS using ε-support vector regression for ovarian cancer identification",

abstract = "The analysis of protein expression profile using SELDI-TOF-MS can assist early detection of ovarian cancer. The chance to save patient{\textquoteright}s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle these limitations, the ε-Support Vector Regression model to identify ovarian cancer is proposed. We can show that the performance of the model to discriminate the protein expression profile with cancer disease from the normal ones can reach accuracy 99\%, specificity 99\% and sensitivity 100\%. This result shows that the model is promising for SELDI-TOFMS analysis in Ovarian Cancer identification.",

keywords = "Ovarian Cancer, SELDI-TOF-MS, Support Vector Regression",

author = "Isye Arieshanti and Yudhi Purwananto",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 15th International Conference on Biomedical Engineering, ICBME 2013 ; Conference date: 04-12-2013 Through 07-12-2013",

year = "2014",

doi = "10.1007/978-3-319-02913-9\_53",

language = "English",

series = "IFMBE Proceedings",

publisher = "Springer Verlag",

pages = "207--210",

editor = "James Goh",

booktitle = "The 15th International Conference on Biomedical Engineering, ICBME 2013",

address = "Germany",

}

Arieshanti, I & Purwananto, Y 2014, Analysis of SELDI-TOF-MS using ε-support vector regression for ovarian cancer identification. in J Goh (ed.), The 15th International Conference on Biomedical Engineering, ICBME 2013. IFMBE Proceedings, vol. 43, Springer Verlag, pp. 207-210, 15th International Conference on Biomedical Engineering, ICBME 2013, Singapore, Singapore, 4/12/13. https://doi.org/10.1007/978-3-319-02913-9_53

Analysis of SELDI-TOF-MS using ε-support vector regression for ovarian cancer identification. / Arieshanti, Isye; Purwananto, Yudhi.
The 15th International Conference on Biomedical Engineering, ICBME 2013. ed. / James Goh. Springer Verlag, 2014. p. 207-210 (IFMBE Proceedings; Vol. 43).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Analysis of SELDI-TOF-MS using ε-support vector regression for ovarian cancer identification

AU - Arieshanti, Isye

AU - Purwananto, Yudhi

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - The analysis of protein expression profile using SELDI-TOF-MS can assist early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle these limitations, the ε-Support Vector Regression model to identify ovarian cancer is proposed. We can show that the performance of the model to discriminate the protein expression profile with cancer disease from the normal ones can reach accuracy 99%, specificity 99% and sensitivity 100%. This result shows that the model is promising for SELDI-TOFMS analysis in Ovarian Cancer identification.

AB - The analysis of protein expression profile using SELDI-TOF-MS can assist early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle these limitations, the ε-Support Vector Regression model to identify ovarian cancer is proposed. We can show that the performance of the model to discriminate the protein expression profile with cancer disease from the normal ones can reach accuracy 99%, specificity 99% and sensitivity 100%. This result shows that the model is promising for SELDI-TOFMS analysis in Ovarian Cancer identification.

KW - Ovarian Cancer

KW - SELDI-TOF-MS

KW - Support Vector Regression

UR - https://www.scopus.com/pages/publications/84928229004

U2 - 10.1007/978-3-319-02913-9_53

DO - 10.1007/978-3-319-02913-9_53

M3 - Conference contribution

AN - SCOPUS:84928229004

T3 - IFMBE Proceedings

SP - 207

EP - 210

BT - The 15th International Conference on Biomedical Engineering, ICBME 2013

A2 - Goh, James

PB - Springer Verlag

T2 - 15th International Conference on Biomedical Engineering, ICBME 2013

Y2 - 4 December 2013 through 7 December 2013

ER -

Analysis of SELDI-TOF-MS using ε-support vector regression for ovarian cancer identification

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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