AIMC Topic: Machine Learning

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Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Trait Genome-to-Phenome (G2P) dimensionality and "breeding context" combine to influence the realised prediction skill of different whole genome prediction (WGP) methods. Theory and empirical evidence both suggest there is likely to be "No Free Lunch...

Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Renal failure
Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk of infection, presenting significant clinical, and economic challenges. Identifying predictive bioma...

Machine-learning-assisted nanopore sensing solution for the determination of matrix metalloproteinase.

Biosensors & bioelectronics
Diabetic kidney disease (DKD) is a serious complication of diabetes patients with long time duration, presenting with albuminuria and/or a reduced estimated glomerular filtration rate (eGFR), and without symptoms of other primary causes of kidney inj...

Combination of Molecular Dynamics Simulations and Machine Learning Reveals Structural Characteristics of Stereochemistry-Specific Interdigitation of Synthetic Monomycoloyl Glycerol Analogs.

Journal of chemical information and modeling
Synthetic monomycoloyl glycerol (MMG) analogs possess robust immunostimulatory activity and are investigated as adjuvants for subunit vaccines in preclinical and clinical studies. These synthetic lipids consist of a glycerol moiety attached to a cory...

Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning.

Journal of translational medicine
BACKGROUND: The pathogenesis of inflammatory bowel disease (IBD) involves complex molecular mechanisms, and achieving clinical remission remains challenging. This study aims to identify IBD-potential biomarkers, analyze their correlation with immune ...

The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.

Lipids in health and disease
BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒glucose index, a marker of insulin resistance, and the composite indicator of obesity.

Keyword-optimized template insertion for clinical note classification via prompt-based learning.

BMC medical informatics and decision making
BACKGROUND: Prompt-based learning involves the additions of prompts (i.e., templates) to the input of pre-trained large language models (PLMs) to adapt them to specific tasks with minimal training. This technique is particularly advantageous in clini...

Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.

BMC medical informatics and decision making
BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient co...

Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients.

Cardiovascular diabetology
BACKGROUND: Coronary Heart Disease (CHD) and Non-Alcoholic Fatty Liver Disease (NAFLD) share overlapping pathogenic mechanisms including adipose tissue dysfunction, insulin resistance, and systemic inflammation mediated by adipokines. However, the sp...

Construction and validation of a risk prediction model for chronic obstructive pulmonary disease (COPD): a cross-sectional study based on the NHANES database from 2009 to 2018.

BMC pulmonary medicine
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major global public health concern, and early screening and identification of high-risk populations are critical for reducing the disease burden. Although several studies have explored the...