AIMC Topic: Machine Learning

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Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance.

Molecules (Basel, Switzerland)
Antimicrobial resistance (AMR) is one of the most significant public health threats today. The need for new antimicrobials against multidrug-resistant infections is growing. The development of computational models capable of predicting new drug-targe...

The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy.

BMC psychiatry
Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise in early identification. This study characterizes Positive Schizotypy Factor (PSF) and Chronic Schizo...

Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques.

Scientific reports
Wheat plays a vital role in Pakistan's economy and food security, making accurate yield forecasting essential for planning and resource management. Traditional approaches-such as manual field surveys and remote sensing-have been widely used, but thei...

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...