AIMC Topic: Plants

Clear Filters Showing 21 to 30 of 202 articles

LMFE: A Novel Method for Predicting Plant LncRNA Based on Multi-Feature Fusion and Ensemble Learning.

Genes
: Long non-coding RNAs (lncRNAs) play a crucial regulatory role in plant trait expression and disease management, making their accurate prediction a key research focus for guiding biological experiments. While extensive studies have been conducted on...

PlantPathoPPI: An Ensemble-based Machine Learning Architecture for Prediction of Protein-Protein Interactions between Plants and Pathogens.

Journal of molecular biology
This study aimed to develop a machine learning-based tool for predicting protein-protein interactions (PPIs) between plant-pathogen systems, addressing the challenges of experimental PPI identification. Identifying PPIs in plant-pathogen interactions...

Novel augmentation techniques using diffusion models for green wall plant health classification.

Computers in biology and medicine
Green walls, vertical plant-based structures, are increasingly popular due to their diverse environmental benefits, including aesthetic enhancement, temperature and humidity regulation, and air pollutant removal. These systems, typically consisting o...

Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI.

Trends in biochemical sciences
Elucidating plant biosynthetic pathways is key to advancing a sustainable bioeconomy by enabling access to complex natural products through synthetic biology. Despite progress from genomic, transcriptomic, and metabolomic approaches, much multiomics ...

PmiProPred: A novel method towards plant miRNA promoter prediction based on CNN-Transformer network and convolutional block attention mechanism.

International journal of biological macromolecules
It is crucial to understand the transcription mechanisms of miRNAs, especially considering the presence of peptides encoded by miRNAs. Since promoters function as the switch for gene transcription, precisely identifying these regions is essential for...

Exploring the impact of natural and human activities on vegetation changes: An integrated analysis framework based on trend analysis and machine learning.

Journal of environmental management
Climate, human activities and terrain are crucial factors influencing vegetation changes. Despite their crucial role, there is a notable lack of research exploring the nonlinear relationships between them and vegetation changes, especially over exten...

Identification, characterization, and design of plant genome sequences using deep learning.

The Plant journal : for cell and molecular biology
Due to its excellent performance in processing large amounts of data and capturing complex non-linear relationships, deep learning has been widely applied in many fields of plant biology. Here we first review the application of deep learning in analy...

Unsupervised learning for lake underwater vegetation classification: Constructing high-precision, large-scale aquatic ecological datasets.

The Science of the total environment
Monitoring underwater vegetation is vital for evaluating lake ecosystem health. Automated data collection and analysis play key roles in achieving large-scale, high-precision, and high-frequency monitoring. While technologies such as unmanned vessels...

Dynamic modelling and predictive position/force control of a plant-inspired growing robot.

Bioinspiration & biomimetics
This paper presents the development and control of a dynamic model for a plant-inspired growing robot, termed the 'vine-robot', using the Euler-Lagrangian method. The unique growth mechanism of the vine-robot enables it to navigate complex environmen...

Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning.

Ecotoxicology and environmental safety
Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co-exposure to MNPs and HMs on plants, which may be related to various influencing factors. In this st...