@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",
}
