AIMC Topic: Machine Learning

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Nonenhanced CT-Based radiomics model enhances PTC detection in Hashimoto's thyroiditis.

BMC cancer
BACKGROUND: Hashimoto's thyroiditis (HT) is a common benign thyroid disease that often coexists with papillary thyroid carcinoma (PTC). Owing to the diffuse changes in the thyroid caused by HT, PTCs can be challenging to detect using conventional ima...

Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.

BMC genomics
BACKGROUND: Genomic Best Linear Unbiased Prediction (GBLUP) assumes that all SNPs contribute equally to genetic variance, including those with minimal impact, limiting its accuracy. A major challenge in animal breeding is to develop more scientific m...

Predictive modeling of flavonoid efficacy against esophageal carcinoma: a comprehensive approach.

Scientific reports
Esophageal carcinoma poses a significant health challenge, particularly due to its notably high prevalence in East Asia, which underscores the urgent need for innovative treatment strategies. Natural flavonoids are polyphenolic compounds with signifi...

Refining cancer prediction with DNA sequencing and combined machine learning approaches.

Scientific reports
A high-accuracy DNA-based cancer risk predictor was developed by blending Logistic Regression with Gaussian Naive Bayes, and its hyperparameters were optimized via grid search. Five cancer types (BRCA1, KIRC, COAD, LUAD, PRAD) were classified in a co...

Predicting the quantum yield of O generation for pteridines and fluoroquinolones using machine learning.

Physical chemistry chemical physics : PCCP
Fluoroquinolones (FQs) are a family of antibiotic drugs well-known for their high photochemical activity: upon UV-vis excitation FQs may produce singlet oxygen and/or lose the fluorine atom. Pterins (Ptrs) are a class of organic photosensitizers with...

Predicting the risk of asthma development in youth using machine learning models.

PloS one
Asthma is a chronic respiratory disease characterized by wheezing and difficulty breathing, which disproportionally affects 4.7 million children in the U.S. Currently, there is a lack of asthma predictive models for youth with good performance. This ...

Machine learning-based prediction of metabolic dysfunction-associated steatotic liver disease using National Health and Nutrition Examination Survey (NHANES) data.

PloS one
OBJECTIVE: With the global increase in obesity rates and lifestyle changes, metabolic dysfunction-associated steatotic liver disease (MASLD) has become a prevalent chronic liver disorder, affecting approximately 25% of the global population. This dis...

Predicting PFAS Diffusion Coefficients with Active Learning and Molecular Dynamics.

Environmental science & technology
Per- and polyfluoroalkyl substances (PFAS) are over 14 000 synthetic compounds with exceptional environmental persistence. Used extensively in industrial and consumer applications, PFAS resist degradation and accumulate in environmental media and liv...

HlightReaxMD: A Machine Learning-Augmented Multiscale Analysis Framework for Radiation Chemistry Dynamics and Damage Prediction.

Journal of chemical information and modeling
Molecular dynamics (MD) simulations are currently widely used to study large-scale displacement cascades based on massive simulation trajectories. However, when the irradiation process involves the complex chemical reactions, effectively analyzing an...

Dynamic Changes in Metabolic Syndrome Scores and New-Onset Stroke Risk in Middle-Aged and Older Adults: A Nationwide Prospective Cohort Study in China Aligned With Predictive, Preventive, and Personalized Medicine.

Journal of the American Heart Association
BACKGROUND: Despite the established link between metabolic syndrome (MetS) and stroke incidence, the effects of dynamic and cumulative MetS scores on stroke risk among middle-aged and older populations in China remain inadequately explored. Furthermo...