一种改进二次结构预测的投票方案

ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010 Pub Date : 2010-05-16 DOI:10.1109/AICCSA.2010.5586931

J. Taheri, Albert Y. Zomaya

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引用次数: 3

摘要

本文提出了一种改进蛋白质二级结构预测的新方法，即SSVS。在这项工作中，我们训练了一个径向基函数神经网络，将不同二级结构预测技术发现的不同答案结合起来，产生更优的答案。SSVS通过了该领域三个著名的基准测试。结果表明，即使在复杂序列的情况下，所提出的技术也具有优越性。

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A voting scheme to improve the secondary structure prediction

This paper presents a novel approach, namely SSVS, to improve the secondary structure prediction of proteins. In this work, a Radial Basis Function Neural Network is trained to combine different answers found by different secondary structure prediction techniques to produce superior answers. SSVS is tested with three of the well-known benchmarks in this field. The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.

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ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010

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