@inproceedings{957ba33cfd6c4889b4c76de8d9e6ec98,
title = "Protein secondary structure prediction using large margin methods",
abstract = "Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesn't only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learning non-linear discriminant via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.",
keywords = "Large margin approach, Probabilistic graphical models, Protein secondary structure prediction, Sequence labeling problem",
author = "Buzhou Tang and Xuan Wang and Xiaolong Wang",
year = "2009",
doi = "10.1109/ICIS.2009.8",
language = "英语",
isbn = "9780769536415",
series = "Proceedings of the 2009 8th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2009",
publisher = "IEEE Computer Society",
pages = "142--146",
booktitle = "Proceedings of the 2009 8th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2009",
address = "美国",
note = "8th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2009 ; Conference date: 01-06-2009 Through 03-06-2009",
}