کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6369857 | 1623836 | 2015 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique
ترجمه فارسی عنوان
دقیق در شناسایی سیلیکون سایت های ساکسیونیلت پروتئین با استفاده از تکنیک یادگیری نیمه نظارت تکراری
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کلمات کلیدی
پروتئین سوکسیینیل شده، نمونه های مثبت تنها یادگیری، ویژگی های چندگانه،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم کشاورزی و بیولوژیک (عمومی)
چکیده انگلیسی
As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Theoretical Biology - Volume 374, 7 June 2015, Pages 60-65
Journal: Journal of Theoretical Biology - Volume 374, 7 June 2015, Pages 60-65
نویسندگان
Xiaowei Zhao, Qiao Ning, Haiting Chai, Zhiqiang Ma,