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• W2521877993 abstract "Accurate classification on protein structural is playing an important role in Bioinformatics. An increase in evidence demonstrates that a variety of classification methods have been employed in such a field. In this research, the features of amino acids composition, secondary structure's feature, and correlation coefficient of amino acid dimers and amino acid triplets have been used. Flexible neutral tree (FNT), a particular tree structure neutral network, has been employed as the classification model in the protein structures' classification framework. Considering different feature groups owing diverse roles in the model, impact factors of different groups have been put forward in this research. In order to evaluate different impact factors, Impact Factors Scaling (IFS) algorithm, which aim at reducing redundant information of the selected features in some degree, have been put forward. To examine the performance of such framework, the 640, 1189, and ASTRAL datasets are employed as the low-homology protein structure benchmark datasets. Experimental results demonstrate that the performance of the proposed method is better than the other methods in the low-homology protein tertiary structures." @default.
• W2521877993 created "2016-09-30" @default.
• W2521877993 creator A5030950144 @default.
• W2521877993 creator A5036479222 @default.
• W2521877993 creator A5046454314 @default.
• W2521877993 date "2017-09-01" @default.
• W2521877993 modified "2023-10-17" @default.
• W2521877993 title "Classification of Protein Structure Classes on Flexible Neutral Tree" @default.
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• W2521877993 doi "https://doi.org/10.1109/tcbb.2016.2610967" @default.
• W2521877993 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28113983" @default.
• W2521877993 hasPublicationYear "2017" @default.
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