AIMC Topic: Machine Learning

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Machine Learning Tackles the Challenge of Powder X-ray Diffraction Indexing for All Crystal Systems.

Journal of chemical information and modeling Sep 22, 2025
The indexing of powder X-ray diffraction (PXRD) in unknown structure determinations is a critical yet challenging step in crystallography, particularly for low-symmetry systems (e.g., monoclinic, triclinic) and/or large unit cell systems ( > 1000 Å)...
X-Ray Diffraction Powder Diffraction Machine Learning Algorithms
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Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability.

Critical care explorations Sep 22, 2025
OBJECTIVES: RBC transfusion in anemic patients with sustainable tolerance may cause harm, emphasizing the need for reliable metrics that quantify adequacy (oxygen delivery ≥ demand) and sustainability (oxygen delivery remains adequate without transfu...
Middle Aged Humans Machine Learning Heart Rate Retrospective Studies Anemia Trauma Centers Clinical Decision-Making Female Erythrocyte Transfusion Wounds and Injuries Resuscitation Adult Male
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ADPO: automatic-differentiation-assisted parametric optimization.

Journal of pharmacokinetics and pharmacodynamics Sep 22, 2025
Automatic differentiation (AD), a key method for accurately and efficiently computing derivatives in modern machine learning, is now implemented in Phoenix® NLME™ 8.6 for the first time and applied to the first-order conditional estimation extended l...
Least-Squares Analysis Voriconazole Models, Biological Algorithms Humans Computer Simulation Machine Learning
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Integrating pollution indices, spatial interpolation, and machine learning for soil contamination analysis along the Zarqa River, Jordan.

Environmental monitoring and assessment Sep 22, 2025
This study assesses soil contamination along the Zarqa River (ZR) in Jordan by integrating pollution indices, geostatistical interpolation, and machine learning models. We collected 34 soil samples from agricultural lands within the study area. Sampl...
Machine Learning Soil Environmental Monitoring Soil Pollutants Environmental Pollution Rivers Jordan
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Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models.

Journal of medical Internet research Sep 22, 2025
BACKGROUND: Early prediction of treatment outcomes for patients with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) undergoing extended therapy is crucial for enhancing clinical prognoses and preventing the transmission of this ...
Tuberculosis, Multidrug-Resistant Rifampin Adult Male Middle Aged Machine Learning Logistic Models Retrospective Studies Treatment Outcome Female Antitubercular Agents Humans
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Machine Learning and Shapley Additive Explanations Value Integration for Predicting the Prognostic of Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Model Development and Evaluation Study.

JMIR medical informatics Sep 22, 2025
BACKGROUND: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a rare disease with no accurate prognostic tools to predict the prognosis of patients.
Anti-N-Methyl-D-Aspartate Receptor Encephalitis Retrospective Studies Female Young Adult Adult Male Adolescent Humans Machine Learning Prognosis
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A predictive model developed to classify Leishmania promastigotes at two distinct life stages using MALDI-TOF mass spectrometry.

Archives of microbiology Sep 22, 2025
Investigating the molecular differences between procyclic (non-infective) and metacyclic (infective) promastigotes is essential for understanding the Leishmania life cycle in the sandfly vector and may aid in identifying molecular markers specific to...
Machine Learning Support Vector Machine Life Cycle Stages Neural Networks, Computer Leishmania Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Protozoan Proteins
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A machine learning framework for estimating the probability of blacklegged tick population establishment in eastern Canada using Earth observation data.

PloS one Sep 22, 2025
Ixodes scapularis ticks are the primary vector of Lyme disease (LD) in North America, and their range has expanded into southeastern and southcentral Canada with climate change. This study presents a comprehensive machine learning (ML) framework to e...
Animals Canada Lyme Disease Machine Learning Probability Ixodes
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Identification of ferroptosis-related genes as diagnostic biomarkers and therapeutic targets in sorafenib-resistant HCC via machine learning and experimental validation.

International immunopharmacology Sep 21, 2025
Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant health burden with poor survival rates due to late diagnosis and limited treatment options. Sorafenib, a multi-kinase inhibitor, is the standard first-line therapy for a...
Carcinoma, Hepatocellular Gene Expression Regulation, Neoplastic Ferroptosis Cell Line, Tumor Liver Neoplasms Machine Learning Humans Drug Resistance, Neoplasm Autophagy-Related Protein-1 Homolog Biomarkers, Tumor Intracellular Signaling Peptides and Proteins Coenzyme A Ligases Antineoplastic Agents Kelch-Like ECH-Associated Protein 1 Sorafenib
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Prediction of polycystic ovary syndrome using machine learning with SFS and Boruta feature selection: an explainable AI approach.

Systems biology in reproductive medicine Sep 21, 2025
Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting numerous women of reproductive age, characterized by a variety of clinical and biochemical features. Accurate classification and diagnosis of PCOS remains challenging due to t...
Polycystic Ovary Syndrome Machine Learning Humans Female Artificial Intelligence Adult
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