Super Granular Shrink-SVM Feature Elimination (Super GS-SVM-FE) Model for Protein Sequence Motif Information Extraction

Author(s):  
Bernard Chen ◽  
Stephen Pellicer ◽  
Phang C. Tai ◽  
Robert Harrison ◽  
Yi Pan
2020 ◽  
pp. 1306-1327
Author(s):  
Gowri Rajasekaran ◽  
Rathipriya R

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.


Author(s):  
Gowri Rajasekaran ◽  
Rathipriya R

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.


2016 ◽  
Vol 7 (3) ◽  
pp. 56-68 ◽  
Author(s):  
Gowri R. ◽  
Rathipriya R.

The main goal of this paper is to compare the motif information extracted from clusters and biclusters of the protein using Motif Comparator. The clusters and biclusters are obtained using the PSO k-means algorithm. The functions of the proteins are preferably found from their motif information. The Motif Comparator is used to detect the clusters and biclusters, to locate the Significant Amino Acids present, to find the highly homologous cluster. The motif information acquired is based on the structure homogeneity of the protein sequence. The homogeneity is evaluated based on their secondary structure similarity of the protein.


Author(s):  
Pedro Gabriel Ferreira ◽  
Paulo Jorge Azevedo

Protein sequence motifs describe, through means of enhanced regular expression syntax, regions of amino-acids that have been conserved across several functionally related proteins. These regions may have an implication at the structural and functional level of the proteins. Sequence motif analysis can bring significant improvements towards a better understanding of the protein sequence-structure-function relation. In this chapter we review the subject of mining deterministic motifs from protein sequence databases. We start by giving a formal definition of the different types of motifs and the respective specificities. Then, we explore the methods available to evaluate the quality and interest of such patterns. Examples of applications and motif repositories are described. We discuss the algorithmic aspects and different methodologies for motif extraction. A briefly description on how sequence motifs can be used to extract structural level information patterns is also provided.


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