ارائه روش استخراج ویژگی شبه‌کوواریانسی مبتنی بر تبدیل موجک جهت کشف نشانگر حیاتی از الگوهای پروتئینی سرطان تخمدان
محورهای موضوعی : مهندسی برق و کامپیوترحسین منتظری کردی 1 , محمدحسین میران‌بیگی 2 , محمدحسن مرادی 3
1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
3 - دانشگاه صنعتی امیرکبیر
کلید واژه: پروتئین‌شناسیتشخیص الگوتبدیل موجک گسستهتابع وزن شبه‌کوواریانسینشانگر حیاتی,
چکیده مقاله :
تغییرات پاتولوژیک درون یک عضو حیاتی به‌صورت الگوهای پروتئینی در خون انعکاس می‌یابد. طیف‌سنجی جرمی بهعنوان یک ابزار اندازه‌گیری قدرتمند در تولید الگوهای پروتئینی از خون شناخته شده است. داده‌های حاصل از این تکنیک بهعنوان داده‌هایی با ابعاد و همبستگی بالا در نظر گرفته می‌شوند که ویژگی‌های کلیدی مهم برای محققان، پیک‌های موجود در طیف می‌باشند. با توجه به این خصوصیات رفتاری داده، یک روش تحلیل مناسب مثل تبدیل موجک نیاز می‌باشد. در این تحقیق، روش جدیدی جهت استخراج ویژگی از داده طیف جرمی با هدف کاهش بعد و همبستگی آن ارائه شده است که مبتنی بر تبدیل موجک گسسته و انتخاب ويژگی شبه‌کوواریانسی می‌باشد. با روش پیشنهادی، نشانگرهای حیاتی مناسب از مجموعه داده‌های مورد مطالعه برای سرطان تخمدان که از انستیتو ملی سرطان آمریکا اخذ شده است، از روی طیف جرمی بازسازی‌شده استخراج گردیده است که منجر به حصول نتایج تشخیصی بالا با استفاده از معیارهای ارزشیابی استاندارد شده است. با استفاده از روش‌های طبقه‌بندی مختلف، روش پیشنهادی جدید منجر به دقت تشخیص 98، نرخ قطعیت 97 و حساسیت 98 درصد شده است.
Pathological changes within an organ can be reflected as proteomic patterns in blood. The mass spectrometry has been used as powerful tools to generate proteomic patterns from serum. The produced profiles can be viewed as high dimensional and correlation data for which the features of scientific interest are the peaks. Due to this complexity of data, an appropriate analysis method is needed such as wavelet transform. In this study, we proposed a pseudo-covariance wavelet-based feature extraction method for dimension reduction and de-correlation between mass spectra data. Our algorithm was applied to datasets of ovarian cancer obtained from the National Cancer Institute of USA. The proposed algorithm was used to extract the set of proteins as potential biomarkers in each dataset from reconstructed mass spectra. The selected biomarkers were able to diagnose ovarian cancer patients from non-cancer with high accurate results using standard diagnosis criteria. Using different classification algorithms, our approach yielded an accuracy of 98%, specificity of 97%, and sensitivity of 98%.
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