Research Article
Species Identification Using Part of DNA Sequence: Evidence from Machine Learning Algorithms
@ARTICLE{10.4108/eai.3-12-2015.2262476, author={Taha Alhersh and Brahim Belhaouari Samir and Hamada Al-Absi and Abdullah Alorainy and Belloui Bouzid}, title={Species Identification Using Part of DNA Sequence: Evidence from Machine Learning Algorithms}, journal={EAI Endorsed Transactions on Future Intelligent Educational Environments}, volume={2}, number={9}, publisher={ACM}, journal_a={FIEE}, year={2016}, month={5}, keywords={machine learning, species identification, dna sequences, feature selection}, doi={10.4108/eai.3-12-2015.2262476} }
- Taha Alhersh
Brahim Belhaouari Samir
Hamada Al-Absi
Abdullah Alorainy
Belloui Bouzid
Year: 2016
Species Identification Using Part of DNA Sequence: Evidence from Machine Learning Algorithms
FIEE
EAI
DOI: 10.4108/eai.3-12-2015.2262476
Abstract
In biological studies, species identification is considered one of the most important issues. Several methods have been suggested to identify species using the whole DNA sequences. In this study, we present new insights for species identification using only part of the DNA sequence. The Clustering k-Nearest Neighbor (K-C-NN) and Support Vector Machine (SVM) classifiers were used to test and evaluate the improved statistical features extracted from DNA sequences for four species (Aquifex aeolicus, Bacillus subtilis, Aeropyrum pernix and Buchnera sp). The results show that part of DNA sequences can be used to identify species.
Copyright © 2015 T. Alhersh et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.