Volume 7, Issue 4 (Journal of Control, V.7, N.4 Winter 2014)                   JoC 2014, 7(4): 53-62 | Back to browse issues page

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Abstract:   (6963 Views)
This paper presents a density-based clustering approach for data classification of protein folding. The method is shown to perform better as compared with the conventional methods with respect to computational speed and robustness against noise. Protein clustering is known to be one of the important challenges of prediction and identification of protein’s properties and its performances. Due to recent advances in sequence detection devices, many proteins have been discovered which requires an automatic protein clustering system. An automatic protein clustering method is thus presented here which is based on folding and information extracted from the output features of the sequence. Further, fuzzy data fusion is used to combine the clustered information in order to improve the clustering performance.
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Type of Article: Research paper | Subject: Special
Received: 2015/02/26 | Accepted: 2015/02/26 | Published: 2015/02/26

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