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

文献类型： 外文期刊

第一作者： Sghaier, Nesrine

作者： Sghaier, Nesrine;Qu, Mingnan;Sghaier, Nesrine;Essemine, Jemaa;Qu, Mingnan;Sghaier, Nesrine;Ben Ayed, Rayda;Ben Ayed, Rayda;Gorai, Mustapha;Ben Marzoug, Riadh;Rebai, Ahmed

作者机构：

关键词： data fusion; machine learning; evidence theory; ARF-binding sites; Chenopodium quinoa

期刊名称：PLANTS-BASEL （ 影响因子：4.5； 五年影响因子：4.8 ）

ISSN：

年卷期： 2023 年 12 卷 1 期

页码：

收录情况： SCI

摘要： 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.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序