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Volume 12
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10.3390/plants12010071
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Article

An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa

by
Nesrine Sghaier
1,2,3,
Jemaa Essemine
2,
Rayda Ben Ayed
4,5,
Mustapha Gorai
6,
Riadh Ben Marzoug
7,
Ahmed Rebai
7 and
Mingnan Qu
1,2,*
1
National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
2
CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
3
Laboratory of Advanced Technology and Intelligent Systems, National Engineering School of Sousse, Sousse 4023, Tunisia
4
Department of Agronomy and Plant Biotechnology, National Institute of Agronomy of Tunisia (INAT), 43 Avenue Charles Nicolle, 1082 El Mahrajène, University of Carthage-Tunis, Tunis 1082, Tunisia
5
Laboratory of Extremophile Plants, Centre of Biotechnology of Borj-Cédria, B.P. 901, Hammam Lif 2050, Tunisia
6
Higher Institute of Applied Biology Medenine, University of Gabes, Medenine 4119, Tunisia
7
Laboratory of Molecular and Cellular Screening Processes, Sfax Biotechnology Center, B.P 1177, Sfax 3018, Tunisia
*
Author to whom correspondence should be addressed.
Plants 2023, 12(1), 71; https://doi.org/10.3390/plants12010071
Submission received: 15 November 2022 / Accepted: 26 November 2022 / Published: 23 December 2022
(This article belongs to the Special Issue Plant Synthetic Biology and Plant Transcriptome)
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Abstract

Quinoa constitutes among the tolerant plants to the challenging and harmful abiotic environmental factors. Quinoa was selected as among the model crops destined for bio-saline agriculture that could contribute to the staple food security for an ever-growing worldwide population under various climate change scenarios. The auxin response factors (ARFs) constitute the main contributors in the plant adaptation to severe environmental conditions. Thus, the determination of the ARF-binding sites represents the major step that could provide promising insights helping in plant breeding programs and improving agronomic traits. Hence, determining the ARF-binding sites is a challenging task, particularly in species with large genome sizes. In this report, we present a data fusion approach based on Dempster–Shafer evidence theory and fuzzy set theory to predict the ARF-binding sites. We then performed an “In-silico” identification of the ARF-binding sites in Chenopodium quinoa. The characterization of some known pathways implicated in the auxin signaling in other higher plants confirms our prediction reliability. Furthermore, several pathways with no or little available information about their functions were identified to play important roles in the adaptation of quinoa to environmental conditions. The predictive auxin response genes associated with the detected ARF-binding sites may certainly help to explore the biological roles of some unknown genes newly identified in quinoa.
Keywords: data fusion; machine learning; evidence theory; ARF-binding sites; Chenopodium quinoa data fusion; machine learning; evidence theory; ARF-binding sites; Chenopodium quinoa

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MDPI and ACS Style

Sghaier, N.; Essemine, J.; Ayed, R.B.; Gorai, M.; Ben Marzoug, R.; Rebai, A.; Qu, M. An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa. Plants 2023, 12, 71. https://doi.org/10.3390/plants12010071

AMA Style

Sghaier N, Essemine J, Ayed RB, Gorai M, Ben Marzoug R, Rebai A, Qu M. An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa. Plants. 2023; 12(1):71. https://doi.org/10.3390/plants12010071

Chicago/Turabian Style

Sghaier, Nesrine, Jemaa Essemine, Rayda Ben Ayed, Mustapha Gorai, Riadh Ben Marzoug, Ahmed Rebai, and Mingnan Qu. 2023. "An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa" Plants 12, no. 1: 71. https://doi.org/10.3390/plants12010071

APA Style

Sghaier, N., Essemine, J., Ayed, R. B., Gorai, M., Ben Marzoug, R., Rebai, A., & Qu, M. (2023). An Evidence Theory and Fuzzy Logic Combined Approach for the Prediction of Potential ARF-Regulated Genes in Quinoa. Plants, 12(1), 71. https://doi.org/10.3390/plants12010071

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