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In this paper, we propose a novel clustering and motif modeling framework for analyzing large number of protein family using k-mer. Our approach of using k-mers utilizes both occurring frequency and position information of k-mers that essential for classification yet not fully used in previous methods. We found that the structure has close relationship between motif of protein family and hence well...
In this paper, we introduce a rapid method to classify protein structures. Instead of using the 3D structures of proteins directly, we used 2D inner-distance matrices converted from the 3D protein structures and extracted feature vectors from the 2D inner-distance matrices. We divided the matrices into a training dataset and a testing dataset. We then employed a 2D Hidden Markov Model (2DHMM) to classify...
The classification of G-Protein Coupled Receptor (GPCR) sequences is an important problem that arises from the need to close the gap between the large number of orphan receptors and the relatively small number of annotated receptors. Equally important is the characterization of GPCR Class A subfamilies and gaining insight into the ligand interaction since GPCR Class A encompasses a very large number...
In the post-genome era, as an essential filternative of experimental method, the computational method is becoming popular. The prediction of protein structural class from protein sequence becomes one of research's concerns because the knowledge of protein structural class can simplify and accelerate in the computational determination of the spatial structure of a newly identified protein. As one of...
This paper focuses on protein sequences family classification from Gracilaria changii seaweed species using back-propagation classifier. Classification of protein sequence family is to infer the function of an unknown protein by analysing its structural similarity to a given family of proteins. The use of sequence alignment technique to classify the protein sequence is less efficient because the entire...
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