کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
531150 | 869814 | 2006 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sequence-driven features for prediction of subcellular localization of proteins
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Prediction of the cellular location of a protein plays an important role in inferring the function of the protein. Feature extraction is a critical part in prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present a method for extracting useful features from protein sequence data. The method employs local and global pairwise sequence alignment scores as well as composition-based features. Five different features are used for training support vector machines (SVMs) separately and a weighted majority voting makes a final decision. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. Comparing the prediction accuracy of various feature extraction methods, provides a biological insight into the location of targeting information. Our experimental results confirm that our feature extraction methods are very useful for predicting subcellular localization of proteins.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 39, Issue 12, December 2006, Pages 2301-2311
Journal: Pattern Recognition - Volume 39, Issue 12, December 2006, Pages 2301-2311
نویسندگان
Jong Kyoung Kim, Sung-Yang Bang, Seungjin Choi,