AIMC Topic: Stress, Physiological

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Computational strategies in systems-level stress response data analysis.

Biological chemistry
Stress responses in biological systems arise from complex, dynamic interactions among genes, proteins, and metabolites. A thorough understanding of these responses requires examining not only changes in individual molecular components but also their ...

Machine learning based prediction by PlantCdMiner and experimental validation of cadmium-responsive genes in plants.

Journal of hazardous materials
Plants have evolved diverse adaptive mechanisms to sense and respond to environmental stimuli such as cadmium stress. The regulation of gene expression plays a critical role in plant responses to abiotic stress. However, homologous genes from differe...

Optimizing Plant Alkaloid Biosynthesis under Drought Stress: Regulatory Mechanisms and Biotechnological Strategies.

Journal of plant physiology
Global climate change exacerbates drought stress, severely affecting plant growth, agricultural productivity, and the biosynthesis of secondary metabolites. Alkaloids, nitrogenous compounds with diverse biological activities, hold substantial medicin...

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food...

Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis.

Nature communications
Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms....

AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy.

Sensors (Basel, Switzerland)
This study investigates plant stress assessment by integrating advanced sensor technologies and Artificial Intelligence (AI). Multi-sensor data-including electrical impedance spectroscopy, temperature, and humidity-were used to capture plant physiolo...

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.

Briefings in bioinformatics
Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identification of single nucleotide polymorphisms (SNPs) associated with abiotic stress traits (ABST-SNPs) in rice is crucial for developing resistant rice varieti...

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Briefings in bioinformatics
Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly identifying previously unknown single-nucleotide polymorphisms (SNPs) in the rice genome is a critical...

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants.

Frontiers in bioscience (Landmark edition)
Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, lig...