Species identification using part of DNA sequence: Evidence from machine learning algorithms

Taha Alhersh; Abdullah Alorainy; Brahim Belhaouari Samir; Hamada R.H. Al-Absi; Belloui Bouzid

doi:10.4108/eai.3-12-2015.2262476

Species identification using part of DNA sequence: Evidence from machine learning algorithms

Taha Alhersh, Abdullah Alorainy, Brahim Belhaouari Samir, Hamada R.H. Al-Absi, Belloui Bouzid

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

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-CNN) 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.

Original language	English
Journal	EAI International Conference on Bio-inspired Information and Communications Technologies (BICT)
DOIs	https://doi.org/10.4108/eai.3-12-2015.2262476
Publication status	Published - 2015
Externally published	Yes
Event	9th EAI International Conference on Bio-Inspired Information and Communications Technologies, BICT 2015 - New York City, United States Duration: 3 Dec 2015 → 5 Dec 2015

Keywords

DNA sequences
Feature selection
Machine learning
Species identification

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10.4108/eai.3-12-2015.2262476

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title = "Species identification using part of DNA sequence: Evidence from machine learning algorithms",

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-CNN) 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.",

keywords = "DNA sequences, Feature selection, Machine learning, Species identification",

author = "Taha Alhersh and Abdullah Alorainy and Samir, {Brahim Belhaouari} and Al-Absi, {Hamada R.H.} and Belloui Bouzid",

note = "Publisher Copyright: {\textcopyright} 2016 ICST.; 9th EAI International Conference on Bio-Inspired Information and Communications Technologies, BICT 2015 ; Conference date: 03-12-2015 Through 05-12-2015",

year = "2015",

doi = "10.4108/eai.3-12-2015.2262476",

language = "English",

journal = "EAI International Conference on Bio-inspired Information and Communications Technologies (BICT)",

issn = "2411-6777",

publisher = "ICST",

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TY - JOUR

T1 - Species identification using part of DNA sequence

T2 - 9th EAI International Conference on Bio-Inspired Information and Communications Technologies, BICT 2015

AU - Alhersh, Taha

AU - Alorainy, Abdullah

AU - Samir, Brahim Belhaouari

AU - Al-Absi, Hamada R.H.

AU - Bouzid, Belloui

PY - 2015

Y1 - 2015

N2 - 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-CNN) 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.

AB - 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-CNN) 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.

KW - DNA sequences

KW - Feature selection

KW - Machine learning

KW - Species identification

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U2 - 10.4108/eai.3-12-2015.2262476

DO - 10.4108/eai.3-12-2015.2262476

M3 - Conference article

AN - SCOPUS:85052166878

SN - 2411-6777

JO - EAI International Conference on Bio-inspired Information and Communications Technologies (BICT)

JF - EAI International Conference on Bio-inspired Information and Communications Technologies (BICT)

Y2 - 3 December 2015 through 5 December 2015

ER -

Species identification using part of DNA sequence: Evidence from machine learning algorithms

Abstract

Keywords

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