AIMC Topic: Machine Learning

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Machine learning intelligently selects feature values to construct a sensor array based on tri-functional Mn-doped covalent organic polymer nanozymes for identifying flavonoid compounds.

Food chemistry
Herein, a novel tri-functional Mn-doped covalent organic polymer nanozyme (Mn-COP) with peroxidase-like, oxidase-like, and laccase-like activities was synthesized. The strong reducing power of flavonoids inhibited the tri-enzymes-like activity from M...

Machine learning and multi-omics integration reveal TRPV2 as a central regulator in bicuspid aortic valve calcification.

Biochemical and biophysical research communications
BACKGROUND: Bicuspid aortic valve (BAV), the most common congenital heart defect, is strongly predisposed to early calcification, yet the molecular drivers remain poorly defined. This study aims to identify the functional role of transient receptor p...

Probing the Gate-Opening Transition in the Bacterial ClpP Peptidase Using Molecular Dynamics Simulations and Machine Learning.

Biochemistry
Preserving proteome integrity is crucial for maintaining cell viability across all kingdoms of life. The bacterial caseinolytic protease (ClpP) plays a critical role in maintaining protein homeostasis by degrading misfolded or damaged proteins within...

Predicting the Ionization Behavior of Drugs in Tissue in MALDI and MALDI-2 Mass Spectrometry Imaging Using Machine Learning.

Analytical chemistry
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and its most common application, MALD-MS imaging (MSI), are widely used techniques in the analysis of intact biomolecules. In the context of pharmaceutical research, MALDI-MSI i...

Metabolite Identification Data in Drug Discovery, Part 2: Site-of-Metabolism Annotation, Analysis, and Exploration for Machine Learning.

Molecular pharmaceutics
The ability to pinpoint and predict sites of metabolism (SoMs) is essential for designing and optimizing effective and safe bioactive small molecules. However, the number of molecules with annotated SoMs is limited, hindering the advancement of data-...

Interpretability of automated machine learning methods in psychological research: A tutorial with AutoGluon in Python.

Behavior research methods
Integrating artificial intelligence into psychological research represents a significant direction in contemporary psychology. Utilizing supervised and unsupervised machine learning techniques can further aid in understanding the nonlinear relationsh...

A comparative study highlights superiority of LSTM in crop genomic prediction.

Planta
We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic pre...

Intelligent optimization of track and field teaching using machine learning and wearable sensors.

Scientific reports
Traditional track and field education relies heavily on subjective assessment and manual feedback systems, creating critical barriers to personalized instruction in large-scale educational settings. This study presents a novel machine learning framew...

Enhancing indoor monitoring of visually impaired people using temporal convolutional network with optimization model in IoT environment.

Scientific reports
The Internet of Things (IoT) has emerged as a powerful technology in various fields, including healthcare, assisting the elderly and disabled individuals. Solution-based IoT is widely utilized in healthcare support in diverse aspects of their daily l...

An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients.

Scientific reports
Increasing water scarcity and climate variability have intensified the need for precise agricultural irrigation management. Accurate estimation of crop coefficients (Kc) is critical for determining crop water requirements, especially in arid and semi...