scholarly journals A Modified Markov Clustering Approach for Protein Sequence Clustering

2008 ◽  
pp. 110-120
Author(s):  
Lehel Medvés ◽  
László Szilágyi ◽  
Sándor M. Szilágyi
Keyword(s):  
Protein Sequence ◽  
Sequence Clustering ◽  
Markov Clustering ◽  
Clustering Approach

The Parallel Maximal Cliques Algorithm for Protein Sequence Clustering

2009 ◽  
Vol 6 (7) ◽  
pp. 1368-1372 ◽  
Author(s):  
Khalid Jaber ◽  
Nur'Aini Abdul Ras ◽  
Rosni Abdullah
Keyword(s):  
Protein Sequence ◽  
Sequence Clustering

SEQOPTICS: A Protein Sequence Clustering Method

2006 ◽  
Author(s):  
Yonghui Chen ◽  
Kevin D. Reilly ◽  
Alan P. Sprague ◽  
Zhijie Guan
Keyword(s):  
Protein Sequence ◽  
Clustering Method ◽  
Sequence Clustering

Extraction of Protein Sequence Motif Information using Bio-Inspired Computing

2020 ◽  
pp. 1306-1327
Author(s):  
Gowri Rajasekaran ◽  
Rathipriya R
Keyword(s):  
Protein Sequence ◽  
Protein Complexes ◽  
Genetic Disorder ◽  
Hereditary Diseases ◽  
Sequence Motif ◽  
Data Mining Technique ◽  
Conserved Sequence ◽  
Clustering Approach ◽  
Related Proteins ◽  
Motif Information

Nowadays there are many people affected by the genetic disorder, hereditary diseases, etc. The protein complexes and their functions are detected, in order to find the irregularity in the gene expression. In a group of related proteins, there exist some conserved sequence patterns (motifs) either functionally or structurally similar. The main objective of this work is to find the motif information from the given protein sequence dataset. The functionalities of the proteins are ideally found from their motif information. Clustering approach is a main data mining technique. Besides the clustering approach, the biclustering is also used in many Bioinformatics related research works. The PSO K-Means clustering and biclustering approach is proposed in this work to extract the motif information. The Motif is extracted based on the structure homogeneity of the protein sequence. In this work, the clusters and biclusters are compared based on homogeneity and motif information extracted. This study shows that biclustering approach yields better result than the clustering approach.

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