Machine learning for phytopathology: from the molecular scale towards the network scale

Y Wang, M Zhou, Q Zou, L Xu - Briefings in Bioinformatics, 2021 - academic.oup.com
With the increasing volume of high-throughput sequencing data from a variety of omics
techniques in the field of plant–pathogen interactions, sorting, retrieving, processing and …

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

B Karan, S Mahapatra, SS Sahu, DM Pandey… - Frontiers in Plant …, 2023 - frontiersin.org
Introduction Plant–microbe interactions play a vital role in the development of strategies to
manage pathogen-induced destructive diseases that cause enormous crop losses every …

Identification and Detection of Rice Plant Diseases by Using Neural Network

RK Dubey, DK Choubey - … Conference on Frontiers in Computing and …, 2023 - Springer
The most serious disease that can affect paddy plants is blast disease. All over the world, it
results in enormous yield losses. A fungus that attacks the plant's leaves, nodes, and grains …

RBD-AIIoT: Rice Blasts Detection Combining AI & IoT

M Vidhya, D Samb, A Vidhya - 2022 International Conference …, 2022 - ieeexplore.ieee.org
Rice blast disease is the most common disease in rice-growing areas in the world, and it is
the most serious in India. Rice is threatened by a number of illnesses. For precise disease …

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

K Taha - bioRxiv, 2023 - biorxiv.org
This survey paper provides an in-depth analysis of various machine learning techniques
and algorithms that are utilized in the detection of PPI (Protein-Protein Interactions). For …

Recent Advances in Applications of Support Vector Machines in Fungal Biology

S Modak, A Lahorkar, J Valadi - … in Fungal Biology: Current Methods in …, 2022 - Springer
Abstract Machine learning methods have been an especially useful and cost-effective way of
predicting in fungal biology. Rapid identification of human fungal infections is a highly active …