AIMC Topic: Biomarkers

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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...

Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

Scientific reports
Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With the rising prevalence and increasing associated chronic complications, identifying and understanding ...

Exploring the link between the ZJU index and sarcopenia in adults aged 20-59 using NHANES and machine learning.

Scientific reports
Sarcopenia, characterized by progressive loss of muscle mass and function, is a growing public health concern. The ZJU index, a novel metabolic marker, integrates lipid metabolism and glucose regulation parameters. While its association with metaboli...

Metabolomic biomarkers could be molecular clocks in timing stroke onset.

Scientific reports
The preferred treatment for acute ischaemic stroke (AIS) is intravenous thrombolysis (IVT) administered within 4.5 hours (h) of symptom onset. This study aimed to identify metabolomic biomarkers for distinguishing AIS patients within 4.5 h of symptom...

Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.

Nature communications
Extracellular vesicles (EVs) in plasma are composed of exosomes, microvesicles, and apoptotic bodies. We report a plasma EV enrichment strategy using magnetic beads called Mag-Net. Proteomic interrogation of this plasma EV fraction enables the detect...

Proteomic risk scores for predicting common diseases using linear and neural network models in the UK biobank.

Scientific reports
Plasma proteomics provides a unique opportunity to enhance disease prediction by capturing protein expression patterns linked to diverse pathological processes. Leveraging data from 2,923 proteins measured in 53,030 UK Biobank participants, we develo...

Identification of aging-related biomarkers and immune infiltration analysis in renal stones by integrated bioinformatics analysis.

Scientific reports
Renal stones (RS) are common urologic condition with unclear pathogenesis. Role of aging-related differentially expressed genes (ARDEGs) in RS remains poorly understood. This study aims to identify potential aging-related biomarkers for RS, explore t...

A supervised machine learning approach with feature selection for sex-specific biomarker prediction.

NPJ systems biology and applications
Biomarkers are crucial in aiding in disease diagnosis, prognosis, and treatment selection. Machine learning (ML) has emerged as an effective tool for identifying novel biomarkers and enhancing predictive modelling. However, sex-based bias in ML algor...

Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics.

Scientific reports
Cholesterol metabolism-related genes (CMRGs) have been associated with osteoarthritis (OA), but their specific regulatory mechanisms remain unclear. This study aimed to investigate the role of CMRGs in OA and provide new insights into its treatment. ...

Identification of plasma proteins associated with seizures in epilepsy: A consensus machine learning approach.

PloS one
Blood-based biomarkers in epilepsy could constitute important research tools advancing neurobiological understanding and valuable clinical tools for better diagnosis and follow-up. An interesting question is whether biomarker patterns could contribut...