AIMC Topic: Machine Learning

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Machine learning prediction prior to onset of mild cognitive impairment using T1-weighted magnetic resonance imaging radiomic of the hippocampus.

Asian journal of psychiatry
BACKGROUND: Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in...

Improving real-time forecasting of bay water quality by integrating in-situ monitoring, machining learning, and process-based modeling.

Journal of environmental management
Frequent occurrences of disasters such as red tides significantly threaten bay ecosystems, making near real-time water quality forecasting crucial for disaster warning and decision-making. Conventional techniques, such as process-based modeling and i...

Predicting response to anti-VEGF therapy in neovascular age-related macular degeneration using random forest and SHAP algorithms.

Photodiagnosis and photodynamic therapy
PURPOSE: This study aimed to establish and validate a prediction model based on machine learning methods and SHAP algorithm to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration ...

Microbial degradation potential of microplastics in urban river sediments: Assessing and predicting the enrichment of PE/PP-degrading bacteria using SourceTracker and machine learning.

Journal of environmental management
Microplastic mitigation strategies that adapt to various actual aquatic environments require the ability to predict their microbial degradation potential. However, the sources and enrichment characteristics of the degrading bacteria in the plastisphe...

Neuronal Waveform Classification in Multielectrode Recordings Using Machine Learning Techniques and Multidimensional Analysis.

International journal of neural systems
Extracellular recordings of neuronal spikes are crucial for studying brain activity. These signals are typically classified based on firing patterns and waveform shape, particularly trough-to-peak duration. While useful, this method oversimplifies th...

Empirically Transformed Energy Patterns: A novel approach for capturing fNIRS signal dynamics in pain assessment.

Computers in biology and medicine
The accurate assessment of pain in clinical settings is challenging due to its subjective nature. In this study, we used functional near-infrared spectroscopy (fNIRS) to measure brain activity by detecting changes in blood oxygenation. Leveraging the...

A novel in silico approach for predicting unbound brain-to-plasma ratio using machine learning-based support vector regression.

Computers in biology and medicine
The blood-brain barrier (BBB) functions as a vital protective mechanism, restricting the entry of substances and xenobiotics into the central nervous system (CNS). Consequently, BBB penetration is a critical aspect of absorption, distribution, metabo...

Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation.

Journal of environmental management
In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of...

Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics.

Obstetrics and gynecology
OBJECTIVE: To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical chara...

Machine learning-based bioactivity prediction of porphyrin derivatives: molecular descriptors, clustering, and model evaluation.

Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology
Understanding the relationship between molecular structure and bioactivity is crucial for optimizing porphyrin-based therapeutics. By integrating cheminformatics techniques with machine learning models, our work enables the efficient classification o...