کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6487106 | 1364 | 2015 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A semi-supervised learning approach for RNA secondary structure prediction
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Biology and Chemistry - Volume 57, August 2015, Pages 72-79
Journal: Computational Biology and Chemistry - Volume 57, August 2015, Pages 72-79
نویسندگان
Haruka Yonemoto, Kiyoshi Asai, Michiaki Hamada,