PROTEOMIC PATTERN EXTRACTION FOR THE DIAGNOSIS OF BREAST CANCER FROM LASER MASS SPECTRA DATA USING DATA MINING ALGORITHM

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MONTAZERI KORDI H.; MIRAN BEYGI M.H.; MORADI M.H.; NAJAFI M.

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Journal: Lasers in Medicine Year:2008 | Volume:5 | Issue:2 (28) Page(s): 16-25

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Title

PROTEOMIC PATTERN EXTRACTION FOR THE DIAGNOSIS OF BREAST CANCER FROM LASER MASS SPECTRA DATA USING DATA MINING ALGORITHM

Author(s)

MONTAZERI KORDI H. | MIRAN BEYGI M.H. | MORADI M.H. | NAJAFI M. | Issue Writer Certificate

Keywords

BREAST CANCERQ4

PROTEOMICSQ4

MASS SPECTRAQ4

DATA MINING ALGORITHMQ4

BIOMARKERQ4

PATTERN RECOGNITIONQ4

Abstract

Background/ Objective: A major problem in the treatment of cancer is the lack of an appropriate method for the early diagnosis of the disease. The BREAST CANCER is a widespread disease within the population of women, and its early diagnosis can greatly prevent the mortality rate. At present, there is no appropriate tumor marker for early diagnosis of the disease. The chemical reaction within an organ may be reflected in the form of proteomic patterns in the serum, sputum, or urine. The surface-enhanced laser desorption/ ionization time-of-flight mass spectrometry is a valuable tool for extracting proteomic patterns from biological samples. A major challenge in analysis of such patterns is the presentation of a DATA MINING ALGORITHM to select appropriate BIOMARKERs to distinguish between healthy and cancer cases.Materials and Methods: In this research, the data corresponding to proteomic patterns of serum from patients with BREAST CANCER was analyzed. Using a mathematical model and discrete wavelet transform, baseline and electrical noises were eliminated in the preprocessing stage with subsequent normalization of the MASS SPECTRA.Our hybrid DATA MINING ALGORITHM is based on a statistical test, class separability measure, and peak scoring. With our method, the best protein subset was selected from 13488 data points while maintaining the valuable information and discriminative power. The selected feature subset was then used for the detection of BIOMARKERs.Results: Using the method of k-fold cross validation, the samples under study were divided randomly into two sets namely the learning and test sets. We identified the least threshold value of 1.96. The DATA MINING ALGORITHM was applied to the remaining data points from thresholding step. Then, the best feature subset was selected which included high power discriminatory BIOMARKERs. Using linear discriminant analysis (LDA), 19 proteins were selected as BIOMARKERs that were able to discriminate healthy and cancer samples with accuracy of 100%, specificity of 100%, and sensitivity of 100%.Conclusion: With the generation of complete information from biological specimens, we can use these to diagnose the diseases showing poor markers such as cancer. Disease diagnosis is an example of PATTERN RECOGNITION. In this paper, we have introduced a DATA MINING ALGORITHM to select the best feature subset from protein patterns. Our proposed method has shown to have a good discriminative power with reduction of the number of BIOMARKERs. Our results suggest that the appropriate selection of significant proteins have an important effect for BIOMARKER identification in the correct diagnosis of the disease.

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APA: Copy

MONTAZERI KORDI, H., MIRAN BEYGI, M.H., MORADI, M.H., & NAJAFI, M.. (2008). PROTEOMIC PATTERN EXTRACTION FOR THE DIAGNOSIS OF BREAST CANCER FROM LASER MASS SPECTRA DATA USING DATA MINING ALGORITHM. LASER IN MEDICINE, 5(2 (28)), 16-25. SID. https://sid.ir/paper/113492/en

Vancouver: Copy

MONTAZERI KORDI H., MIRAN BEYGI M.H., MORADI M.H., NAJAFI M.. PROTEOMIC PATTERN EXTRACTION FOR THE DIAGNOSIS OF BREAST CANCER FROM LASER MASS SPECTRA DATA USING DATA MINING ALGORITHM. LASER IN MEDICINE[Internet]. 2008;5(2 (28)):16-25. Available from: https://sid.ir/paper/113492/en

IEEE: Copy

H. MONTAZERI KORDI, M.H. MIRAN BEYGI, M.H. MORADI, and M. NAJAFI, “PROTEOMIC PATTERN EXTRACTION FOR THE DIAGNOSIS OF BREAST CANCER FROM LASER MASS SPECTRA DATA USING DATA MINING ALGORITHM,” LASER IN MEDICINE, vol. 5, no. 2 (28), pp. 16–25, 2008, [Online]. Available: https://sid.ir/paper/113492/en

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