AIMC Topic: Machine Learning

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An Innovative Machine Learning Based Multistage Signal Amplification Method Breaks through the Detection Limits of Conventional Optical Sensors.

ACS sensors
Surface plasmon resonance (SPR) enables in situ, label-free, real-time molecular detection. However, traditional SPR biosensors require complex and precise large-scale equipment, with detection limits constrained by multiple factors, complicating and...

Computational prediction of mutagenicity through comprehensive cell painting analysis.

Mutagenesis
The mutagenicity of chemical compounds is a key consideration in toxicology, drug development, and environmental safety. Traditional methods such as the Ames test, while reliable, are time-intensive and costly. With advances in imaging and machine le...

Machine Learning-Based Biomarker Identification for Early Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease.

The Journal of clinical endocrinology and metabolism
CONTEXT: Metabolic dysfunction-associated steatotic liver disease (MASLD) is an umbrella term for simple hepatic steatosis and the more severe metabolic dysfunction-associated steatohepatitis. The current reliance on liver biopsy for diagnosis and a ...

Harnessing Allostery to Modulate Protein-Protein Interactions: From Function to Therapeutic Innovations.

Journal of molecular biology
Protein-protein interactions (PPIs) are ubiquitous mediators of cellular functions, and their dysregulation is central to numerous pathological conditions. Traditional drug discovery strategies targeting PPIs directly have faced considerable obstacle...

Assessing the toxicological impact of DEGDB plasticizer exposure on glioblastoma multiforme via network toxicology, machine learning and in vitro methods.

Environmental pollution (Barking, Essex : 1987)
Diethylene glycol dibenzoate (DEGDB) is a novel environmentally friendly plasticizer. However, toxicological studies on DEGDB remain limited, and its potential harmful effects on the malignant progression of glioblastoma are still unclear. Further sy...

Spatiotemporal evolution and driver analysis of wastewater greenhouse gas emissions in Chinese mainland: Insights and future trends.

Environmental research
Wastewater greenhouse gas (GHG) emissions represent a complex system characterized by distinct spatial-temporal patterns influenced by various drivers. This study examined the spatiotemporal heterogeneity of wastewater GHG emission intensity and tota...

Benchtop Vis-NIR spectroscopy meets machine learning for multi-task analysis in Hongmeiren citrus: Geographical origin identification and antioxidant component quantification.

Food chemistry
Due to geographical indication advantage of Hongmeiren (HMR) citrus, economically motivated origin fraud has emerged, alongside significant differences in antioxidant components. This study employed benchtop visible and near-infrared (Vis-NIR) spectr...

Beetle-inspired responsive photonic microgel assemblies for multi-sensing enhanced by machine learning.

Biosensors & bioelectronics
Bioinspired photonic hydrogels hold promise as sensors; however, their use in triple-analyte sensing optical devices has been minimally explored. Temperature, serum Fe levels, and X-ray doses are critical factors for predicting and monitoring medical...

DPP-IV inhibitory peptides from highland barley via machine learning and multi-scale validation.

Food chemistry
Highland barley has shown potential in regulating blood glucose and may serve as a natural source of dipeptidyl peptidase-IV (DPP-IV) inhibitors. In this study, machine learning (Gradient Boosting Decision Trees) and virtual screening were employed t...

Characterize the dynamic changes of volatile compounds during the roasting process of Wuyi rock tea (Shuixian) integrating GC-IMS and GC × GC-O-MS combined with machine learning.

Food chemistry
Understanding aroma compounds' changes during Shuixian roasting is vital for scientific guidance. This study used gas chromatography-ion mobility spectrometry (GC-IMS) and two-dimensional gas chromatography-olfactory-mass spectrometry (GC × GC-O-MS) ...