AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Hydrogen-Ion Concentration

Showing 1 to 10 of 182 articles

Clear Filters

Deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensor for the rapid freshness sensing of aquatic product.

Food research international (Ottawa, Ont.)
Rapid detection of freshness particularly in aquatic products demands efficient sensing strategies. Here, a novel deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensing platform was established for rapid freshness assay of aqua...

Application of explainable machine learning in the production of pullulan by Aureobasidium pullulans CGMCCNO.7055.

International journal of biological macromolecules
The application of machine learning in pullulan biofermentation has demonstrated significant potential. Explainable machine learning enhances model transparency and interpretability by revealing the relationships between variables. In this study, we ...

A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction.

Journal of chemical information and modeling
The potential of hydrogen (pH) influences the function of the enzyme. Measuring or predicting the optimal pH (pH) at which enzymes exhibit maximal catalytic activity is crucial for enzyme design and application. The rapid development of enzyme mining...

Enhanced nitrogen prediction and mechanistic process analysis in high-salinity wastewater treatment using interpretable machine learning approach.

Bioresource technology
This study introduces an interpretable machine learning framework to predict nitrogen removal in membrane bioreactor (MBR) treating high-salinity wastewater. By integrating Shapley additive explanations (SHAP) with Categorical Boosting (CatBoost), we...

Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature.

Briefings in bioinformatics
An accurate deep learning predictor is needed for enzyme optimal temperature (${T}_{opt}$), which quantitatively describes how temperature affects the enzyme catalytic activity. In comparison with existing models, a new model developed in this study,...

Extraction of pectin from watermelon rinds using sequential ultrasound-microwave technique: Optimization using RSM and ANN modeling and characterization.

International journal of biological macromolecules
This study aimed to optimize pectin extraction from watermelon (Citrullus lanatus) rind using sequential ultrasound-microwave assisted extraction (UMAE) with artificial neural network (ANN) and response surface methodology (RSM). The effects of pH, s...

Predicting surface soil pH spatial distribution based on three machine learning methods: a case study of Heilongjiang Province.

Environmental monitoring and assessment
Comprehensive and accurate acquisition of surface soil pH spatial distribution information is essential for monitoring soil degradation and providing scientific guidance for agricultural practices. This study focused on Heilongjiang Province in China...

ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties.

Nature communications
The UniProt database is a valuable resource for biocatalyst discovery, yet predicting enzymatic functions remains challenging, especially for low-similarity sequences. Identifying superior enzymes with enhanced catalytic properties is even harder. To...

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.

Journal of chemical information and modeling
Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versatility as a solvent. However, accurately predicting...

Optimization process of coffee pulp wines combined with the artificial neural network and response surface methodology.

Scientific reports
Coffee pulp wine was made from coffee pulp. The level range of fermentation factors was determined by one-factor experiment. The significance factors affecting fermentation were screened by Plackett-Burman and steepest climbing experiments, which wer...