SOLVENT ACCESSIBILITY PREDICTION

using

High Order Conditional Random Fields


FREE FOR ACADEMIC RESEARCH

How ACRF Will Benefit You

Efficient

ACRF is more effcient than ACCpro.

Accurate

With ACRF, the accuracy of SA prediction is improved.

EASY-TO-USE

ACRF is robust and easy to use.

OUR SOFTWARE FEATURES
 

Accurate.

Efficient.

Easy to use.

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More on ACRF

ACRF is developed for predicting solvent accessibility which is based on a high order conditional random field model. This method not only models the interdependency among adjacent residues, but also exploits the correlation among long range residues. First, it is discovered that different secondary structures has different periodicity of solvent accessibility. Therefore, high order features are firstly introduced, and different orders of models are used on different secondary structures. Second, this study achieves improvement by using new features such as sequence conservation, contact numbers. In conclusion, this method has higher accuracy than other existing ones. In addition, through case studies, it is shown that high order terms supplement local features with long range correlations, and correct the wrong positions predicted by bidirectional recurrent neural network and chain condition random field.

NEWS
 



WELCOME

  • Welcome to use ACRF for protein solvent accessibility prediction.

Contact Us

Bioinformatics Lab, Institute of Computing Technology
Chinese Academy of Sciences

6, Kexueyuan South Road, Zhongguancun
Beijing, China

(86)10-62600817

FALCON@ict.ac.cn