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2008

Improved general regression network for protein domain boundary prediction

8 years 9 months ago
Improved general regression network for protein domain boundary prediction
Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark ...
Paul D. Yoo, Abdur R. Sikder, Bing Bing Zhou, Albe
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2008
Where BMCBI
Authors Paul D. Yoo, Abdur R. Sikder, Bing Bing Zhou, Albert Y. Zomaya
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