تبدیل توالی پروتئین به تصویر جهت طبقه¬بندی با شبکه عصبی کانولوشنی
محورهای موضوعی : عمومىرضا احسن 1 , منصور ابراهیمی 2 * , روح الله دیانت 3
1 - عضو هیات علمی
2 - دانشکده علوم پایه - دانشگاه قم - قم - ایران
3 - دانشکده فنی مهندسی - دانشگاه قم - قم – ایران
کلید واژه: تبدیل توالی پروتئین به تصویر, فیلتر گابور, شبکه عصبی کانولوشنی, طبقه¬بندی توالی پروتئین.,
چکیده مقاله :
از آنجا که روشهای مخصوص طبقه‌‌بندی توالی یادگیری ماشین، جهت طبقه‌‌بندی پروتئین‌‌های سالم و سرطانی موفق نبودند بنابراین یافتن راهکاری برای بازنمایی این توالی‌‌ها جهت طبقه بندی افراد سالم و مریض با رویکردهای یادگیری عمیق ضرورت تام دارد. در این مطالعه، روشهای مختلف بازنمایی توالی پروتئین، جهت طبقه‌‌بندی توالی پروتئین افراد سالم و سرطان خون، مورد بررسی قرار گرفته است. نتایج نشان داد که تبدیل حروف اسید آمینه به بردار ویژگی یکبعدی در طبقه بندی 2 کلاس موفق نبود و فقط یک کلاس مریض تشخیص داده شد. با تغییر بردار ویژگی به‌‌صورت اعداد رنگی دقت تشخیص کلاس سالم کمی بهبود یافت. روش بازنمایی توالی پروتئینی به‌‌صورت یکپارچه دودویی، با ابتکار حفظ دنباله توالی در دو حالت یکبعدی و دوبعدی(تصویر با اعمال فیلتر گابور)، نسبت به روشهای قبلی موثرتر بود. بازنمایی توالی پروتئین به شکل تصویر دودویی با اعمال فیلتر گابور با دقت 100% توالی پروتئین افراد سالم و 98.6% توالی پروتئین افراد دارای سرطان خون را طبقه‌‌بندی کرد. یافته‌‌های این تحقیق نشان داد که بازنمایی توالی پروتئین به شکل تصویر دودویی با اعمال فیلتر گابور، می‌‌تواند به‌‌عنوان روش موثر جدید دربازنمایی توالی‌‌های پروتئینی جهت طبقه‌‌بندی، ارایه نماید.
Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study different methods of protein sequence representation for classification of protein sequence of healthy individuals and leukemia have been studied. Results showed that conversion of amino acid letters to one-dimensional feature vectors in classification of 2 classes was not successful and only one disease class was detected. By changing the feature vector to colored numbers, the accuracy of the healthy class recognition was slightly improved. The binary protein sequence representation method was more efficient than the previous methods with the initiative of sequencing the sequences in both one-dimensional and two-dimensional (image by Gabor filtering). Protein sequence representation as binary image was classified by applying Gabor filter with 100% accuracy of the protein sequence of healthy individuals and 98.6% protein sequence of those with leukemia. The findings of this study showed that the representation of protein sequence as binary image by applying Gabor filter can be used as a new effective method for representation of protein sequences for classification
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