AIMC Topic: Machine Learning

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Machine Learning on Toxicogenomic Data Reveals a Strong Association Between the Induction of Drug-Metabolizing Enzymes and Centrilobular Hepatocyte Hypertrophy in Rats.

International journal of molecular sciences
Centrilobular hepatocyte hypertrophy is frequently observed in animal studies for chemical safety assessment. Although its toxicological significance and precise mechanism remain unknown, it is considered an adaptive response resulting from the induc...

Rapid identification of tumor patients with PG-SGA ≥ 4 based on machine learning: a prospective study.

BMC cancer
BACKGROUND: Malnutrition is common in cancer patients and worsens treatment and prognosis. The Patient-Generated Subjective Global Assessment (PG-SGA) is the best tool to evaluate malnutrition, but it is complicated has limited its routine clinical u...

Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils.

Scientific reports
The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties...

Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study.

Scientific reports
Machine learning algorithms that integrate multiple biomarkers are increasingly used in disease detection, yet economic considerations are often overlooked. Medial vascular calcification (mVC), a pathology associated with elevated cardiovascular risk...

Prediction of bloodstream infection using machine learning based primarily on biochemical data.

Scientific reports
Early diagnosis of bloodstream infection (BSI) is crucial for informed antibiotic use. This study developed a machine learning approach for early BSI detection using a comprehensive dataset from Rigshospitalet, Denmark (2010-2020). The dataset includ...

Accurate Cancer Diagnosis and Treatment Monitoring through Multiplexed Profiling of Protein Markers on Small Extracellular Vesicles.

ACS nano
The detection of small extracellular vesicles (sEVs) is currently a pivotal liquid biopsy approach for noninvasive cancer diagnosis. However, the lack of adequate specificity and sensitivity, as well as labor-intensive purification and analysis proce...

Machine Learning-Enabled Time-Resolved Nanozyme-Encoded Recognition of Endogenous Mercaptans for Disease Diagnosis.

Analytical chemistry
With their important role in regulating intracellular redox balance and maintaining cell homeostasis, endogenous mercaptans are recognized as biomarkers of many diseases in clinical practice, and thus establishing efficient yet simple methods to dist...

A Digital Score Based on Circulating-Tumor-Cells-Derived mRNA Quantification and Machine Learning for Early Colorectal Cancer Detection.

ACS nano
Circulating tumor cells (CTCs) serve as valuable biomarkers in tumor circulation, carrying essential primary tumor information. The purification of CTCs from peripheral blood samples and the analysis of their characteristic molecules enable the detec...

Mapping the Edges of Mass Spectral Prediction: Evaluation of Machine Learning EIMS Prediction for Xeno Amino Acids.

Analytical chemistry
Mass spectrometry is one of the most effective analytical methods for unknown compound identification. By comparing observed / spectra with a database of experimentally determined spectra, this process identifies compound(s) in any given sample. Unkn...

Machine Learning-Enabled Emotion Recognition by Multisource Throat Signals.

ACS nano
Emotion monitoring plays a crucial role in mental health management. However, traditional methods of emotion recognition predominantly rely on subjective questionnaires or facial expression analyses, which are often inadequate for continuous and high...