AIMC Topic: Biomarkers

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Identification and validation of PANoptosis-related biomarkers in Alzheimer's disease via single-cell RNA sequencing and machine learning.

European journal of medical research
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder with complex underlying mechanisms. PANoptosis, a newly defined form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis, may play a crucial ...

Association of blood-based DNA methylation of lncRNAs with Alzheimer's disease diagnosis.

Clinical epigenetics
BACKGROUND: DNA methylation has shown great potential in Alzheimer's disease (AD) blood diagnosis. However, the ability of long non-coding RNAs (lncRNAs), which can be modified by DNA methylation, to serve as noninvasive biomarkers for AD diagnosis r...

Explainable ensemble learning for Epstein-Barr virus risk prediction in ulcerative colitis and Crohn's disease using routine biomarkers.

Scientific reports
Epstein-Barr virus (EBV) exacerbates inflammatory bowel disease (IBD) and is challenging to monitor with invasive or costly tests. We investigated whether explainable machine learning can predict EBV infection from routine clinical data in ulcerative...

TGM1 as a novel signature gene in psoriasis identified by integrative bioinformatics and experimental validation.

Molecular medicine reports
Psoriasis is a systemic immune‑mediated skin disease, typically considered to be incurable. Identification of meaningful biomarkers has been a notable challenge in psoriasis prevention and management. The present study aimed to determine the signatur...

Identification and validation of biomarkers associated with cellular senescence and demethylation in acute myocardial infarction.

Scientific reports
Acute myocardial infarction (AMI) triggered cardiomyocyte senescence and impaired cardiac function. Methylation modifications and related enzymes in patients were also significantly altered, but the association between cellular senescence and demethy...

Identifying key clinical and biochemical predictors of treatment outcomes in inflammatory bowel disease: a real-world evidence study.

Scientific reports
Inflammatory bowel disease (IBD), including Crohn's disease and Ulcerative colitis, often shows variable responses to biological therapies. Identifying the most significant variables for predicting the response to these therapies could help prioritiz...

MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning.

NPJ systems biology and applications
Precision oncology relies on predictive biomarkers for selecting targeted cancer therapies. Network-based properties of proteins, together with structural features such as intrinsic disorder, are likely to shape their potential as biomarkers. We ther...

Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.

Renal failure
Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel tw...

A systematic longitudinal study of microbiome: integrating temporal-spatial dimensions with causal and deep learning models.

BMC genomics
Longitudinal microbiome data provide a unique opportunity to explore dynamic interactions between microbial communities and disease progression. However, these data are often characterized by missing values, sparse signals, and limited interpretabili...

Machine learning identifies inflammation-related diagnostic biomarkers for primary myelofibrosis with clinical validation.

Scientific reports
Primary myelofibrosis (PMF) is a heterogeneous bone marrow disorder, and substantial evidence indicates the involvement of inflammatory mediators in its progression. However, a diagnostic model based on inflammation-related genes has not yet been est...