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

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.

Application of data fusion modeling for the prediction of Auxin response elements in Zea mays for food security purposes

“…To give an automatic score to any unknown detected motif that would be located on the graph, we defined a gradual preference for each region through a set of four propositions: P4(Hi): total confidence in Hi; P3(Hi,Hj): strong preference for hypothesis Hi; P2(Hi,Hj): low preference for Hi; P1(Hi,Hj): total ignorance. The preference level for a hypothesis from P1 to P4 was then gradually represented by a mass value equal to 0, 0.33, 0.67, and 1, respectively [ 9 , 26 ].…”

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

“…Except for the theoretical justifications described above, the evidence theory has also been intensively studied in various real-world applications [24], [25]. However, only a little amount of literature relates to evidential semi-automatic image segmentation [26].…”