The quantitative structure-retention relationship (QSRR) is a significant approach in
chromatography and is used to predict the retention time of unknown compounds. In the …

Metabolite identification in untargeted metabolomics is complex, with the risk of false
positive annotations. This work aims to use machine learning to successively predict the …

Energy consumption in buildings is gradually increasing and accounts for around forty
percent of the total energy consumption. Forecasting the heating and cooling loads of a …

Abstract LC–MS/MS is a major analytical platform for metabolomics, which has become a
recent hotspot in the research fields of life and environmental sciences. By contrast, structure …

Retention time prediction, facilitated by advances in machine learning, has become a useful
tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural …

The retention time (RT) of contaminants of emerging concern (CECs) in liquid
chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database …

Developing risk assessment tools for CAD prediction remains challenging nowadays. We
developed an ML predictive algorithm based on metabolic and clinical data for determining …

Capillary electrophoresis (CE) is an analytical technique widely applied in clinical, industrial,
and scientific laboratories. Discussion of scientists' and specialists' concerns regarding the …

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-
Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship …

The accuracy of the structural annotation of unidentified peaks obtained in metabolomic
analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be …