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Search Results: 1 - 3 of 3 matches for " Khaddouja Boujenfa "
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Consensus Decision for Protein Structure Classification  [PDF]
Khaddouja Boujenfa, Mohamed Limam
Journal of Intelligent Learning Systems and Applications (JILSA) , 2012, DOI: 10.4236/jilsa.2012.43022
Abstract: The fundamental aim of protein classification is to recognize the family of a given protein and determine its biological function. In the literature, the most common approaches are based on sequence or structure similarity comparisons. Other methods use evolutionary distances between proteins. In order to increase classification performance, this work proposes a novel method, namely Consensus, which combines the decisions of several sequence and structure comparison tools to classify a given structure. Additionally, Consensus uses the evolutionary information of the compared structures. Our method is tested on three databases and evaluated based on different criteria. Performance evaluation of our method shows that it outperforms the different classifiers used separately and gives higher classification perfor-mance than a free-alignment method, namely ProtClass.
A comparison of MSA tools
Nadia Essoussi,Khaddouja Boujenfa,Mohamed Limam
Bioinformation , 2008,
Abstract: Multiple sequence alignment (MSA) is essential in phylogenetic, evolutionary and functional analysis. Several MSA tools are available in the literature. Here, we use several MSA tools such as ClustalX, Align-m, T-Coffee, SAGA, ProbCons, MAFFT, MUSCLE and DIALIGN to illustrate comparative phylogenetic trees analysis for two datasets. Results show that there is no single MSA tool that consistently outperforms the rest in producing reliable phylogenetic trees.
Tree-kNN: A Tree-Based Algorithm for Protein Sequence Classification
Khaddouja Boujenfa,,Nadia Essoussi,Mohamed Limam
International Journal on Computer Science and Engineering , 2011,
Abstract: The phylogenomic classification of protein sequences attempts to categorize a given protein within the evolutionary context of the entire family. It involves mainly four steps: selection of homologoussequences, multiple sequence alignment, phylogenetic tree construction and tree-based classification. This supposes that the tree used as a basis of protein classification is correct. Sequence alignment is the first step for tree construction. Thus, the accuracy of the alignment produced should affect the topology of the phylogenetic tree. This work proposes a kNN tree-based algorithm for protein classification, namely Tree-kNN, which uses a phylogenetic tree estimated from pair-wise and multiple alignment approaches.We compare the classification performance of Tree-kNN with an existing method, called TreeNN. Results show that Tree-kNN gives better results than TreeNN. Based on four datasets we show that classification performances of the two algorithms using pair-wise alignment are better than using multiple alignment.
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