نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی کارشناسی ارشد،گروه علوم دامی - دانشکده کشاورزی و محیط زیست، دانشکده کشاورزی، دانشگاه اراک، اراک، ایران.
2 دانشیار، گروه علوم دامی - دانشکده کشاورزی و محیط زیست، دانشکده کشاورزی، دانشگاه اراک، اراک، ایران.
چکیده
هدف این پژوهش مقایسه کارایی و عملکرد روش پیشرفته شبکه عصبی مصنوعی نسبت به تجزیه مؤلفههای اصلی در تفکیک نژادهای مختلف اسب بود. بنابراین، برای شناسایی یک زیرمجموعه از نشانگرهای SNP با بالاترین قدرت تفکیک نژادی و بررسی نحوه اختصاص حیوانات به گروههای نژادی خود از دو روش شبکه عصبی پرسپترون (الدن) و روش کلاسیک تجزیه مؤلفههای اصلی (PCA) استفاده شد. نتایج حاصل نشان داد روش شبکه عصبی (الدن) قادر است که 37 نژاد اسب موردمطالعه در این پژوهش کنونی را، با زیرمجموعه کوچکی از نشانگرهای SNP (8000 نشانگر) و با قدرت تفکیک مشابه با تمام نشانگرهای ژنوم (صحت 98 درصدی)، از همدیگر مجزا و تفکیک کند. روش انتخاب PCA تنها توانست نژادهایی که دارای خاستگاههای متفاوت بودند را شناسایی و تفکیک کند. با توجه به نتایج بهدست‌آمده، روش PCA دارای خطا و ایراد بوده و برای اجرا روی دادههای ژنومی نیاز به تغییرات و اصلاحات دارد. نتایج این پژوهش، رویکردهای عملی را در طراحی آرایههای اقتصادی در تفکیک نژادهای مختلف اسب ارائه میدهد.
کلیدواژه‌ها
عنوان مقاله [English]
Comparing the performance of principal component analysisand Artificial Neural Network methods in identifying the discriminating SNP(s) in different horse breeds of the world
نویسندگان [English]
- Siavash Manzoori 1
- Amir Hossein Khaltabadi Farahani 2
- Mohammad Hossein Moradi 2
- Mehdi Kazemi bon-Chenari 2
1 Department of Animal Science, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Iran.
2 Department of Animal Science, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Iran.
چکیده [English]
The aim of this research was to compare the efficiency and performance of the advanced artificial neural network method with the principal component analysis method in discriminating different horse breeds. In this study, two methods of perceptron neural network (Olden) and the principal component analysis (PCA), were used to identify a subset of SNP markers with the highest breed discrimination potential and to investigate how to assign animals to their breed groups. The results showed that the network method (Olden), is able to separate all the 37 horse breeds with a small subset of SNP markers (8,000 markers) with a same capability to all genomic markers (98% accuracy). The PCA selection method was only able to identify and separate breeds with diverse geographical originations. According to the obtained results, the PCA method is not error-free and depends upon changes and modifications to run on genomic data. The results of this study provide practical approaches in the design of economic arrays for discriminating the different horse breeds.
کلیدواژه‌ها [English]
- Artificial neural network
- Assignment analysis
- Genetic structure
- horse breeds
- Principal Component Analysis
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