Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model

Zhang, Tianyue; Chang, Haowu; Zhang, Borui; Liu, Sifei; Zhao, Tianheng; Zhao, Enshuang; Zhao, Hengyi; Zhang, Hao

doi:10.1016/j.compbiolchem.2020.107401

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…Nowadays, the majority of plants are affected by leaf spots and other leaf diseases. These can be prevented by proper crop management techniques [2]. Various disease resistant plants are being produced and cultivated by biotechnological research techniques to encourage hygiene permanence in leafy vegetable plants [3].…”

Section: Introductionmentioning

confidence: 99%

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

Madderi Sivalingam,

Devi Badabagni

2024

IJEECS

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<div align="center"><span>Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions.</span></div>

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Section: Introductionmentioning

confidence: 99%

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

Madderi Sivalingam,

Devi Badabagni

2024

IJEECS

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“…To develop broad-spectrum and durable solutions, researchers have conducted extensive research work on the MoO-rice pathosystem using various omics approaches. However, elucidating the intricate host-pathogen crosstalk solely through biological experiments remains challenging [6][7][8][9]. The advent of various omics datasets on MoO-rice interactions provides new opportunities to uncover key regulators through computational approaches.…”

Section: Introductionmentioning

confidence: 99%

A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder

Zhao,

Dong,

Zhao

et al. 2023

JoF

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Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between MoO and rice. To address this limitation, we proposed a novel method called Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), which combines weighted gene co-expression network analysis (WGCNA) and graph autoencoder to predict the relationship between MoO–rice multi-omics data. We applied WGAEMRP to construct a MoO–rice multi-omics heterogeneous interaction network, which identified 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins among the key biomolecules. Most of the mined functional modules and enriched pathways were related to gene expression, protein composition, transportation, and metabolic processes, reflecting the infection mechanism of MoO. Compared to previous studies, WGAEMRP significantly improves the efficiency and accuracy of multi-omics data integration and analysis. This approach lays out a solid data foundation for studying the biological process of MoO infecting rice, refining the regulatory network of pathogenic markers, and providing new insights for developing disease-resistant rice varieties.

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“…More than 19 000 different species of fungi can cause plant diseases; a range of epidemic diseases caused by them has deeply affected the process of agricultural production. , Fungal diseases have become one of the most destructive factors that cannot be ignored. For example, Fusarium graminearum, as one of the most devastating diseases of wheat, can infect nearly 25% of crop loss during an outbreak; Botrytis cinerea is ubiquitous in tomatoes, strawberries, and grapes, and it costs 12.6 billion dollars globally to control every year; and Sclerotinia sclerotiorum has a wide range of hosts and can infest more than 400 plants, including important dicot crops such as oils and beans . Furthermore, many fungi also produced mycotoxins, such as aflatoxin, ergotamine, and deoxynivalenol, which would cause great damage to the health of people and livestock …”

Section: Introductionmentioning

confidence: 99%

“…For example, Fusarium graminearum, as one of the most devastating diseases of wheat, can infect nearly 25% of crop loss during an outbreak; 6 Botrytis cinerea is ubiquitous in tomatoes, strawberries, and grapes, and it costs 12.6 billion dollars globally to control every year; 7 and Sclerotinia sclerotiorum has a wide range of hosts and can infest more than 400 plants, including important dicot crops such as oils and beans. 8 Furthermore, many fungi also produced mycotoxins, such as aflatoxin, ergotamine, and deoxynivalenol, which would cause great damage to the health of people and livestock. 9 Until now, the most effective way to control crop pathogenic fungi is the use of fungicides.…”

Section: ■ Introductionmentioning

confidence: 99%

Antifungal Exploration of Quinoline Derivatives against Phytopathogenic Fungi Inspired by Quinine Alkaloids

Chen

et al. 2021

J. Agric. Food Chem.

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Enlightened from our previous work of structural simplification of quinine and innovative application of natural products against phytopathogenic fungi, lead structure 2,8-bis(trifluoromethyl)-4-quinolinol (3) was selected to be a candidate and its diversified design, synthesis, and antifungal evaluation were carried out. All of the synthesized compounds Aa1–Db1 were evaluated for their antifungal activity against four agriculturally important fungi, Botrytis cinerea, Fusarium graminearum, Rhizoctonia solani, and Sclerotinia sclerotiorum. Results showed that compounds Ac3, Ac4, Ac7, Ac9, Ac12, Bb1, Bb10, Bb11, Bb13, Cb1. and Cb3 exhibited a good antifungal effect, especially Ac12 had the most potent activity with EC50 values of 0.52 and 0.50 μg/mL against S. sclerotiorum and B. cinerea, respectively, which were more potent than those of the lead compound 3 (1.72 and 1.89 μg/mL) and commercial fungicides azoxystrobin (both >30 μg/mL) and 8-hydroxyquinoline (2.12 and 5.28 μg/mL). Moreover, compound Ac12 displayed excellent in vivo antifungal activity, which was comparable in activity to the commercial fungicide boscalid. The preliminary mechanism revealed that compound Ac12 might cause an abnormal morphology of cell membranes, an increase in membrane permeability, and release of cellular contents. These results indicated that compound Ac12 displayed superior in vitro and in vivo fungicidal activities and could be a potential fungicidal candidate against plant fungal diseases.

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Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model

Cited by 8 publications

References 41 publications

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder

Antifungal Exploration of Quinoline Derivatives against Phytopathogenic Fungi Inspired by Quinine Alkaloids

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