AIMC Topic: Machine Learning

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Ultrasensitive Detection via Machine Learning-Optimized Bacterial-Imprinted Photoelectrochemical Biosensor with Active/Passive Dual-Mode Validation.

Analytical chemistry
Currently, the existing detection platforms face persistent challenges in achieving reliable bacterial identification within complex matrices, particularly in food and environmental specimens, where matrix interference effects substantially compromis...

A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.

European journal of medical research
Various diseases, such as colon cancer, gastric cancer, celiac, and bleeding, pose a significant risk to the gastrointestinal (GI) tract, which serves as a fundamental component of the human body. It is less invasive to observe the inner part for dis...

Machine learning-based dynamic CEA trajectory and prognosis in gastric cancer.

BMC cancer
BACKGROUND: Static carcinoembryonic antigen (CEA) levels are well‑established prognostic markers in patients with gastric cancer, but the significance of their dynamic trajectories over time has rarely been reported.

Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns.

BMC oral health
BACKGROUND: Craniofacial phenotyping is essential for individualized orthodontic diagnosis and treatment planning. Traditional skeletal classifications, such as the ANB angle, may oversimplify complex relationships among malocclusion types. Machine l...

Tomato ripeness prediction using low resolution portable spectrometer and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Tomato ripeness assessment is critical to ensure optimal product quality. This study proposes a novel approach to predict total soluble solids (TSS) and firmness, and classify tomato ripeness using a low-resolution AS7265x portable spectrometer combi...

Application of metabolomics and MCDM approach in developing a novel strategy for disease diagnosis: A case study in Primary Sjögren's Syndrome.

Journal of pharmaceutical and biomedical analysis
Primary Sjögren's Syndrome (pSS) is a complex autoimmune disease with an unclear etiology. Due to the lack of a single diagnostic gold standard, multidisciplinary and invasive examinations are often required for pSS, underscoring the urgent need for ...

Machine learning combine with nomogram to guide the establishment of endoscopic assistant system for gasless transaxillary endoscopic thyroidectomy.

Annals of medicine
OBJECTIVE: To explore the influence related factors of endoscopic assistant in gasless transaxillary endoscopic thyroidectomy by using machine learning and nomogram, and construct an endoscopic assistant system.

Unraveling the sensory metabolome of blueberries: An integrated metabolomics and machine learning approach across cultivars and geographical origins.

Food chemistry
Consumer-driven blueberry quality improvement requires a deeper understanding of how metabolic composition influences sensory perception. This study integrates untargeted metabolomics and machine learning to identify biomarker metabolites shaping sen...

Biology-aware machine learning for culture medium optimization.

New biotechnology
Cell culture technologies are widely used in academia and industry, yet optimizing culture media remains an art due to the complexity of cell-medium interactions. Machine learning has emerged as a promising solution, but it is hindered by biological ...

Leveraging readily available clinical data with machine learning to predict first-line immunotherapy outcomes in non-small cell lung cancer.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitors (ICIs) are essential first-line treatments for recurrent or metastatic non-small cell lung cancer (NSCLC). However, predicting their effectiveness and the occurrence of immunotherapy-related adverse events (ir...