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Biomarkers

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Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq.

Archives of toxicology
Multicollinearity, characterized by significant co-expression patterns among genes, often occurs in high-throughput expression data, potentially impacting the predictive model's reliability. This study examined multicollinearity among closely related...

Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.

European journal of heart failure
AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abn...

Plasma cell-free RNA signatures of inflammatory syndromes in children.

Proceedings of the National Academy of Sciences of the United States of America
Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circul...

Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases.

Pharmacology & therapeutics
Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanis...

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.

Frontiers in immunology
BACKGROUND: Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen pot...

Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.

Respiratory research
BACKGROUND: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecula...

Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta.

International journal of molecular sciences
Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matri...

Identification of immune-related biomarkers for intracerebral hemorrhage diagnosis based on RNA sequencing and machine learning.

Frontiers in immunology
BACKGROUND: Intracerebral hemorrhage (ICH) is a severe stroke subtype with high morbidity, disability, and mortality rates. Currently, no biomarkers for ICH are available for use in clinical practice. We aimed to explore the roles of RNAs in ICH path...

Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.

The Lancet. Digital health
BACKGROUND: Point-of-care (POC) high-sensitivity cardiac troponin (hs-cTn) assays have been shown to provide similar analytical precision despite substantially shorter turnaround times compared with laboratory-based hs-cTn assays. We applied the prev...