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Published on: April 17, 2012
A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform
Hussain Montazery-Kordy1, Mohammad Hossein Miran-Baygi, Mohammad Hassan Moradi
1Department of Electrical and Computer Engineering, Tarbiat Modares University, P.O. Box 14115-111, Tehran, Iran.
Journal of Zhejiang University. Science. B
|November 7, 2008
Summary
A new bioinformatic tool identifies potential cancer biomarkers from proteomic data. This data-mining approach achieved high accuracy in detecting ovarian and breast cancers, aiding early diagnosis.
Area of Science:
- Bioinformatics
- Proteomics
- Cancer Biomarker Discovery
Background:
- Early cancer detection is crucial for effective treatment.
- Identifying reliable cancer biomarkers remains a significant challenge in clinical practice.
Purpose of the Study:
- To develop a novel bioinformatic tool utilizing data-mining for extracting informative proteins.
- To identify potential protein biomarkers for cancer detection.
Main Methods:
- Utilized two independent datasets from ovarian and breast cancer patients (253 and 167 samples, respectively).
- Employed surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for sample analysis.
- Applied data-mining within the discrete stationary wavelet transform domain, including hard thresholding and distance measures for feature selection.
- Used inverse discrete stationary wavelet transform and two-sided t-test to identify potential biomarkers.
Main Results:
- Identified five proteins as potential ovarian cancer biomarkers with 100% accuracy, sensitivity, and specificity.
- Discovered eight proteins as potential breast cancer biomarkers with 98.26% accuracy, 100% sensitivity, and 95.6% specificity.
Conclusions:
- The developed bioinformatic tool effectively identifies potential cancer biomarkers.
- This tool, combined with high-throughput proteomic data like SELDI-TOF MS, demonstrates high discriminative power for cancer detection.
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