An Improved K-Means Algorithm for DNA Sequence Clustering

In recent years, billions of DNA and protein sequences are subject to sequencing. However, few of them have known structures and functions, most remain unknown. The solution to this problem is to link sequences between them rather than revisit each new sequence independently of other sequences. Thus...

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Veröffentlicht in:Proceedings - International Workshop on Database and Expert Systems Applications S. 39 - 42
Hauptverfasser: Aleb, Nasssima, Labidi, Narimane
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2015
Schlagworte:
Bioinformatics
Classification
Clustering
Clustering algorithms
Clustering methods
Deoxyribonucleic acid
DNA
DNA Sequence Analysis
Expert systems
Gene sequencing
K-means
Proteins
Sequential analysis
Vector quantization
Workshops
ISBN:1467375810, 9781467375818
ISSN:1529-4188, 2378-3915
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Beschreibung
Zusammenfassung:In recent years, billions of DNA and protein sequences are subject to sequencing. However, few of them have known structures and functions, most remain unknown. The solution to this problem is to link sequences between them rather than revisit each new sequence independently of other sequences. Thus, if we manage to assimilate a sequence S1 to another sequence S2 or to a group of previously studied sequences, this will allow us to directly deduce the structure, functions and phylogenetic classification of S2. The purpose of this work is to adapt clustering methods to the specific problem of classification of DNA sequences. We introduce a new method based on K-means clustering for DNA sequences clustering. We begin by explaining and motivating our approach, then we present obtained results.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISBN:1467375810
9781467375818
ISSN:1529-4188
2378-3915
DOI:10.1109/DEXA.2015.27