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گروه‌بندی ژنتیکی گاومیش های بومی آذری و شمالی با روش شبکۀ عصبی SVM

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه علوم دامی، دانشکدۀ کشاورزی، دانشگاه تبریز

2 استادیار، گروه علوم دامی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج

3 استاد، گروه علوم دامی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج

4 دانشیار، گروه علوم دامی، دانشکدۀ کشاورزی، دانشگاه تبریز

5 استاد، گروه علوم دامی، دانشکدۀ کشاورزی، دانشگاه تبریز

چکیده

هدف این تحقیق گروه‌بندی گاومیش‌های استان­های آذربایجان شرقی، غربی و اردبیل از بوم‌جور (اکوتیپ) آذری و استان گیلان از بوم‌جور شمالی و درنهایت قابلیت جداسازی افراد مناطق مختلف با روش یادگیری ماشین بود. به شمار 258 گاومیش از مناطق مختلف دو بوم‌جور شمالی و آذری نمونه‌گیری شد و با استفاده از SNPChip 90K مربوط به شرکت افی متریکس در کشور ایتالیا تعیین ژنوتیپ شد. برای پیش‌بینی عملکرد روش ماشین بردار پشتیبان برای گروه‌بندی افراد، دو روش متریک اعتبارسنجی متقابل و سطح زیر منحنی مشخصۀ عملکرد سامانۀ (AUC) اعمال شد. نتایج آزمون اعتبارسنجی متقابل و سطح زیر منحنی برای گروه‌بندی افراد چهار منطقه به ترتیب 92 و 96 درصد بود که گویای این است که باوجود نزدیک بودن افراد گله‌های مختلف و سخت بودن جداسازی این افراد، روش ماشین بردار پشتیبان با درستی بالایی، توانایی اختصاص دادن افراد به گله­های مربوط به خود را دارد. نتایج آزمون­های اعتبارسنجی متقابل و سطح زیر منحنی مشخصه عملکرد سامانه برای دو بوم‌جور به ترتیب برابر 96 و 98 درصد بود که نشان‌دهندۀ قابلیت جداسازی بهتر دو بوم‌جور است. روش یادگیری ماشین با توجه به این موارد و با پیش‌بینی‌هایی که برای گروه‌بندی هر فرد انجام می­دهد می­تواند در کنترل کیفیت و کاربردهای ژنتیکی کارآمد باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Genetic classification of Azari and North ecotype Buffalo population using SVM method

نویسندگان [English]

  • Zahra Azizi 1
  • Hossein Moradi Shahrbabak 2
  • Mohammad Moradi Shahrbabak 3
  • Abbas Rafat 4
  • Jalil Shodja 5
1 Ph.D. Student, Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Iran
2 Associate Professor, Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Iran
3 Professor, Department of Animal Sciences, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
4 Associate Professor, Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Iran
5 Professor, Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Iran
چکیده [English]

The purpose of this research was to classify buffaloes from different areas of the two Azari (West and East Azarbayjan and Ardabil provinces) and North (Guilan province) ecotypes using support vector machine method. A total of 258 buffalo were sampled and genotyped using the Axiom Buffalo 90K Genotyping Array at the Parco Technologic Padano lab in Italy. Two metric methods of cross validation and the area under the receiver operating characteristic (AUC) were used to determine the predictive performance of support vector machine (SVM) to classify individuals. The results of cross validation and methods for classifying different regions of the two ecotypes (4 provinces) were 92% and 96%, respectively that showed despite the difficulty of identifying individuals from provinces close to each other, support vector machine (SVM) method shows higher accuracy in assigning animals to their herds. Result of two ecotypes showed accuracy about 96% and 98% which represents the better ability to separate the two ecotypes. Machine learning method provides predictions for classification of each individual which can be efficient in quality control and genetic studies.

کلیدواژه‌ها [English]

  • Buffalo
  • Classification
  • SNPChip 90K
  • Support vector Machine
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