Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learn...
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in d...
Innovative identification technologies for hematopoietic stem cells (HSCs) have expanded the scope of stem cell biology. Clinically, the functional quality of HSCs critically influences the safety and therapeutic efficacy of stem cell therapies. Howe...
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively pertur...
Fresh produce packaging (FPP) plays a critical role in protecting fruits and vegetables from various environmental factors. However, the presence, migration, and human health risks of additives in FPP have received limited attention. This study inves...
Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the vali...
Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limi...
Biochimica et biophysica acta. Proteins and proteomics
Apr 11, 2025
Psychrophilic (cold-active) organisms have developed enzymes that facilitate sufficient metabolic activity at low temperatures to sustain life. This occurs through molecular adaptations that tend to increase protein flexibility at the expense of stab...
Algae are cost-effective bioadsorbents for heavy metal remediation, yet their potential is underutilized due to limitations in traditional adsorption models. This study integrates machine learning (ML) techniques with traditional models to predict th...
Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.