AIMC Topic: Machine Learning

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Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations.

Current osteoporosis reports
PURPOSE OF REVIEW: Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underreprese...

Novel Technologies in Preterm Birth Prediction: Current Advances and Ethical Challenges.

Journal of mother and child
Preterm birth (PTB) remains a significant challenge in modern obstetric practice, posing considerable risks to maternal and neonatal health. Despite advancements in medical technology, the incidence of PTB remains high, and its prediction continues t...

Diagnostic and prognostic value of ECG-predicted hypertension-mediated left ventricular hypertrophy using machine learning.

Journal of hypertension
OBJECTIVE: Four hypertension-mediated left ventricular hypertrophy (LVH) phenotypes have been reported using cardiac magnetic resonance (CMR): normal LV, LV remodelling, eccentric and concentric LVH, with varying prognostic implications. The electroc...

Machine Learning-Based Analysis of Differentially Expressed Genes in the Muscle Transcriptome Between Beef Cattle and Dairy Cattle.

International journal of molecular sciences
Muscle is a crucial component of cattle, playing a vital role in determining the final quality of beef. This study aimed to identify candidate genes associated with muscle growth and lipid metabolism in beef and dairy cattle by utilizing the public d...

A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.

Genome biology
Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from ...

Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.

BMC medical imaging
BACKGROUNDS: Delayed cerebral ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH), leading to poor prognosis and high mortality. This study developed a non-contrast CT (NCCT)-based radiomics nomogram for e...

Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.

Scientific reports
Hypoxemia is a common complication associated with anesthesia in painless gastroscopy. With the aging of the social population, the number of cases of hypoxemia among middle-aged and elderly patients is increasing. However, tools for predicting hypox...

Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis.

JMIR cancer
BACKGROUND: Defining optimal adjuvant therapeutic strategies for older adult patients with breast cancer remains a challenge, given that this population is often overlooked and underserved in clinical research and decision-making tools.

Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact.

Frontiers in public health
BACKGROUND: This study investigated the cost-effectiveness and clinical impact of the 3E model (education, empowerment, and economy) in diabetes management using advanced machine learning techniques.

Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.

Frontiers in immunology
INTRODUCTION: Pancreatic cancer (PC) remains a lethal malignancy with limited treatment options. The role of innate immune cell barrier-related genes in PC prognosis is poorly defined. This study aimed to identify prognostic biomarkers, develop a pre...