Title :
A novel self-adaptive algorithm for cancer classification based on feature reduction of SELDI-TOF data using wavelet decomposition
Author :
Maruf, Golam Morshed ; Rashid, Sabrina
Author_Institution :
Dept. of Comput. Sci. & Eng., United Int. Univ., Dhaka, Bangladesh
Abstract :
Surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) is one of the most powerful tool of modern proteomic technology. This high throughput mass spectrometry technology provides large number of complex data with high clinical significance. The protein sequence obtained from this technology is largely used in disease prediction, especially in cancer classification. But the high dimensionality of SELDI-TOF data presents great analytical and computational challenges for such classification using machine learning techniques. In this paper, novel technique has been proposed for dimensionality reduction of the SELDI-TOF data using wavelet decomposition. This technique is self adaptive and independent of the properties of data set. For classification purpose Support Vector Machine (SVM) has been proposed. The performance of the proposed algorithm is evaluated on ovarian and pancreatic cancer data set. The data sets were collected from National Cancer Institute, Center for Cancer Research, USA. A comparative performance analysis with another recently reported algorithm in literature reveals that our method can reduce the dimensionality of the data set more effectively with improved classification accuracy, sensitivity and specificity.
Keywords :
cancer; learning (artificial intelligence); medical computing; pattern classification; support vector machines; time of flight mass spectroscopy; Center for Cancer Research; National Cancer Institute; SELDI-TOF data; SVM; USA; United States of America; cancer classification; classification accuracy; classification sensitivity; classification specificity; comparative performance analysis; dimensionality reduction; disease prediction; feature reduction; high throughput mass spectrometry technology; machine learning technique; ovarian cancer data set; pancreatic cancer data set; protein sequence; proteomic technology; self-adaptive algorithm; support vector machine; surface enhanced laser desorption; surface enhanced laser ionization; time of flight mass spectrometry; wavelet decomposition; Cancer; Copper; Tin; SELDI-TOF; support vector machine; t-test; wavelet decomposition;
Conference_Titel :
Computer and Information Technology (ICCIT), 2011 14th International Conference on
Conference_Location :
Dhaka
Print_ISBN :
978-1-61284-907-2
DOI :
10.1109/ICCITechn.2011.6164823