Implementation of machine learning in DNA barcoding for determining the plant family taxonomy
The DNA barcoding approach has been used extensively in taxonomy and phylogenetics. The differences in certain DNA sequences are able to differentiate and help classify organisms into taxa. It has been used in cases of taxonomic disputes where morphology by itself is insufficient. This research aime...
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Elsevier Ltd
2023
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2-s2.0-85171802354 Riza L.S.; Zain M.I.; Izzuddin A.; Prasetyo Y.; Hidayat T.; Abu Samah K.A.F. Implementation of machine learning in DNA barcoding for determining the plant family taxonomy 2023 Heliyon 9 10 10.1016/j.heliyon.2023.e20161 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171802354&doi=10.1016%2fj.heliyon.2023.e20161&partnerID=40&md5=3867920c858db50b508f95cf47386652 The DNA barcoding approach has been used extensively in taxonomy and phylogenetics. The differences in certain DNA sequences are able to differentiate and help classify organisms into taxa. It has been used in cases of taxonomic disputes where morphology by itself is insufficient. This research aimed to utilize hierarchical clustering, an unsupervised machine learning method, to determine and resolve disputes in plant family taxonomy. We take a case study of Leguminosae that historically some classify into three families (Fabaceae, Caesalpiniaceae, and Mimosaceae) but others classify into one family (Leguminosae). This study is divided into several phases, which are: (i) data collection, (ii) data preprocessing, (iii) finding the best distance method, and (iv) determining disputed family. The data used are collected from several sources, including National Center for Biotechnology Information (NCBI), journals, and websites. The data for validation of the methods were collected from NCBI. This was used to determine the best distance method for differentiating families or genera. The data for the case study in the Leguminosae group was collected from journals and a website. From the experiment that we have conducted, we found that the Pearson method is the best distance method to do clustering ITS sequence of plants, both in accuracy and computational cost. We use the Pearson method to determine the disputed family between Leguminosae. We found that the case study of Leguminosae should be grouped into one family based on our research. © 2023 Elsevier Ltd 24058440 English Article All Open Access; Gold Open Access; Green Open Access |
author |
Riza L.S.; Zain M.I.; Izzuddin A.; Prasetyo Y.; Hidayat T.; Abu Samah K.A.F. |
spellingShingle |
Riza L.S.; Zain M.I.; Izzuddin A.; Prasetyo Y.; Hidayat T.; Abu Samah K.A.F. Implementation of machine learning in DNA barcoding for determining the plant family taxonomy |
author_facet |
Riza L.S.; Zain M.I.; Izzuddin A.; Prasetyo Y.; Hidayat T.; Abu Samah K.A.F. |
author_sort |
Riza L.S.; Zain M.I.; Izzuddin A.; Prasetyo Y.; Hidayat T.; Abu Samah K.A.F. |
title |
Implementation of machine learning in DNA barcoding for determining the plant family taxonomy |
title_short |
Implementation of machine learning in DNA barcoding for determining the plant family taxonomy |
title_full |
Implementation of machine learning in DNA barcoding for determining the plant family taxonomy |
title_fullStr |
Implementation of machine learning in DNA barcoding for determining the plant family taxonomy |
title_full_unstemmed |
Implementation of machine learning in DNA barcoding for determining the plant family taxonomy |
title_sort |
Implementation of machine learning in DNA barcoding for determining the plant family taxonomy |
publishDate |
2023 |
container_title |
Heliyon |
container_volume |
9 |
container_issue |
10 |
doi_str_mv |
10.1016/j.heliyon.2023.e20161 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171802354&doi=10.1016%2fj.heliyon.2023.e20161&partnerID=40&md5=3867920c858db50b508f95cf47386652 |
description |
The DNA barcoding approach has been used extensively in taxonomy and phylogenetics. The differences in certain DNA sequences are able to differentiate and help classify organisms into taxa. It has been used in cases of taxonomic disputes where morphology by itself is insufficient. This research aimed to utilize hierarchical clustering, an unsupervised machine learning method, to determine and resolve disputes in plant family taxonomy. We take a case study of Leguminosae that historically some classify into three families (Fabaceae, Caesalpiniaceae, and Mimosaceae) but others classify into one family (Leguminosae). This study is divided into several phases, which are: (i) data collection, (ii) data preprocessing, (iii) finding the best distance method, and (iv) determining disputed family. The data used are collected from several sources, including National Center for Biotechnology Information (NCBI), journals, and websites. The data for validation of the methods were collected from NCBI. This was used to determine the best distance method for differentiating families or genera. The data for the case study in the Leguminosae group was collected from journals and a website. From the experiment that we have conducted, we found that the Pearson method is the best distance method to do clustering ITS sequence of plants, both in accuracy and computational cost. We use the Pearson method to determine the disputed family between Leguminosae. We found that the case study of Leguminosae should be grouped into one family based on our research. © 2023 |
publisher |
Elsevier Ltd |
issn |
24058440 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access; Green Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809677580619481088 |