AIMC Topic: Principal Component Analysis

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A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Journal of proteome research Jan 7, 2025
Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrome...
Humans Machine Learning Proteomics Blood Proteins Principal Component Analysis Depression, Postpartum Biomarkers Female Adult ROC Curve
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High-throughput, rapid, and non-destructive detection of common foodborne pathogens via hyperspectral imaging coupled with deep neural networks and support vector machines.

Food research international (Ottawa, Ont.) Jan 7, 2025
Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspec...
Escherichia coli Staphylococcus aureus Food Contamination Food Microbiology Neural Networks, Computer Deep Learning Support Vector Machine High-Throughput Screening Assays Hyperspectral Imaging Bacillus cereus Foodborne Diseases Principal Component Analysis Discriminant Analysis
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Trace detection of antibiotics in wastewater using tunable core-shell nanoparticles SERS substrate combined with machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jan 5, 2025
Surface-enhanced Raman scattering (SERS) show great potential for rapid and highly sensitive detection of trace amounts of contamination from the environment in the surface aquatic ecosystem. The widespread use of antibiotics has resulted in serious ...
Silver Ciprofloxacin Metal Nanoparticles Least-Squares Analysis Wastewater Discriminant Analysis Algorithms Anti-Bacterial Agents Machine Learning Spectrum Analysis, Raman Support Vector Machine Water Pollutants, Chemical Principal Component Analysis
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A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks.

Scientific reports Dec 30, 2024
Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity. This research employs a weighted combination of deep neural networks and optimization techniques f...
Principal Component Analysis Multiple Sclerosis Brain Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Neural Networks, Computer Algorithms
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Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction.

Scientific reports Dec 28, 2024
Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annea...
Principal Component Analysis Humans Coronary Artery Disease Neural Networks, Computer Algorithms
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Identification of Phosphodiesterase type 5 inhibitors (PDE5is) analogues using surface-enhanced Raman scattering and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 27, 2024
Phosphodiesterase type 5 inhibitors (PDE5is), primarily used for the treatment of erectile dysfunction, have been severely misused in recent years, posing a threat to public health and safety. This study developed a method that combines Surface-enhan...
Spectrum Analysis, Raman Phosphodiesterase 5 Inhibitors Humans Metal Nanoparticles Machine Learning Support Vector Machine Least-Squares Analysis Algorithms Principal Component Analysis Discriminant Analysis Gold
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Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning.

Analytical biochemistry Dec 27, 2024
Brain tumors are among the most dangerous, due to their location in the organ that governs all life processes. Moreover, the high differentiation of these poses a challenge in diagnostics. Therefore, this study focused on the chemical differentiation...
Meningioma Meningeal Neoplasms Middle Aged Humans Female Spectroscopy, Fourier Transform Infrared Machine Learning Brain Neoplasms Principal Component Analysis Male Glioblastoma
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Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 20, 2024
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classificatio...
Support Vector Machine Food Contamination Principal Component Analysis Least-Squares Analysis Discriminant Analysis Animal Feed Microplastics Machine Learning Algorithms Animals Spectroscopy, Near-Infrared Chickens
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Diagnostic accuracy of texture analysis applied to T- and T-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard.

European journal of radiology Dec 20, 2024
OBJECTIVES: To explore texture analysis' ability on T and T relaxation maps to classify liver fibrosis into no-to-mild liver fibrosis (nmF) versus severe fibrosis (sF) group using machine learning algorithms and histology as reference standard.
Biopsy Image Interpretation, Computer-Assisted Reference Standards Elasticity Imaging Techniques Liver Principal Component Analysis Area Under Curve Echo-Planar Imaging Male Middle Aged Severity of Illness Index ROC Curve Liver Cirrhosis Support Vector Machine Magnetic Resonance Imaging Retrospective Studies Female Aged Adult Humans
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Photonic platform coupled with machine learning algorithms to detect pyrolysis products of crack cocaine in saliva: A proof-of-concept animal study.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 18, 2024
The non-invasive detection of crack/cocaine and other bioactive compounds from its pyrolysis in saliva can provide an alternative for drug analysis in forensic toxicology. Therefore, a highly sensitive, fast, reagent-free, and sustainable approach wi...
Photons Crack Cocaine Male Rats Machine Learning Principal Component Analysis Support Vector Machine Animals Saliva Spectroscopy, Fourier Transform Infrared Proof of Concept Study Pyrolysis
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