AIMC Topic: Machine Learning

Clear Filters Showing 1581 to 1590 of 34417 articles

Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation.

The Science of the total environment
The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial dis...

Knowledge and Structure-Based Drug Design of 15-PGDH Inhibitors.

Journal of medicinal chemistry
PGE2 plays important roles in immune cell function and in potentiating tissue regeneration. 15-PGDH is the key enzyme involved in inactivation of PGE2 and its inhibition therefore provides valuable therapeutic opportunity. We have solved the first co...

Human Microbiome-Based Prediction of Health Effects of Foods via Machine Learning.

Journal of agricultural and food chemistry
Food absorption is dependent on the activities of internal microorganisms. When exploring food functionality, considering the food compounds and their metabolites produced by microbial metabolism is crucial. In this study, we developed a machine lear...

Chemical Space Exploration and Reinforcement Learning for Discovery of Novel Benzimidazole Hybrid Antibiotics.

Journal of chemical information and modeling
Benzimidazole hybrids are promising antibacterial agents, but the growing problem of antibiotic resistance has led to the necessity of developing novel compounds with enhanced antimicrobial activity. This study utilizes AI methods to generate new ant...

Basic Stability Tests of Machine Learning Potentials for Molecular Simulations in Computational Drug Discovery.

Journal of chemical information and modeling
Neural network potentials trained on quantum-mechanical data can calculate molecular interactions with relatively high speed and accuracy. However, not all neural network potentials are suitable for molecular simulations, as they might exhibit instab...

Optimizing ambulance location based on road accident data in Rwanda using machine learning algorithms.

International journal of health geographics
BACKGROUND: The optimal placement of ambulances is critical for ensuring timely emergency medical responses, especially in regions with high accident frequencies. In Rwanda, where road accidents are a leading cause of injury and death, the strategic ...

Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis.

Scientific reports
Nepeta persica is a medicinal plant with significant pharmacological potential, primarily attributed to its high nepetalactone content. Understanding the environmental drivers of nepetalactone biosynthesis is essential for optimizing both cultivation...

The effect of taxonomic, host-dependent features and sample bias on virus host prediction using machine learning and short sequence k-mers.

Scientific reports
Metaviromic studies of potential emerging infection reservoirs led to discovery of many novel viruses. Since metaviromes contain viruses from target host, its food or other sources, fast and robust approaches are needed to predict hosts of unknown vi...

All-flexible chronoepifluidic nanoplasmonic patch for label-free metabolite profiling in sweat.

Nature communications
Wearable sensors allow non-invasive monitoring of sweat metabolites, but their reliance on molecular recognition elements limits both physiological coverage and temporal resolution. Here we report an all-flexible chronoepifluidic surface-enhanced Ram...

Misleading Results in Posttraumatic Stress Disorder Predictive Models Using Electronic Health Record Data: Algorithm Validation Study.

Journal of medical Internet research
BACKGROUND: Electronic health record (EHR) data are increasingly used in predictive models of posttraumatic stress disorder (PTSD), but it is unknown how multivariable prediction of an EHR-based diagnosis might differ from prediction of a more rigoro...