AIMC Topic: Biomarkers

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Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

Diabetes, obesity & metabolism
AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals
BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted th...

Prediction of Interactomic HUB Genes in Periodontitis With Acute Myocardial Infarction.

The Journal of craniofacial surgery
BACKGROUND: Acute myocardial infarction (AMI) risk correlates with C-reactive protein (CRP) levels, suggesting systemic inflammation is present well before AMI. Studying different types of periodontal disease (PD), extremely common in individuals at ...

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

PloS one
Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted pro...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

Journal of affective disorders
BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presenta...

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis.

International journal of molecular sciences
Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of s...

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.

Scientific reports
Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015,...

Enhancing liver fibrosis diagnosis and treatment assessment: a novel biomechanical markers-based machine learning approach.

Physics in medicine and biology
Accurate diagnosis and treatment assessment of liver fibrosis face significant challenges, including inherent limitations in current techniques like sampling errors and inter-observer variability. Addressing this, our study introduces a novel machine...

Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning.

PloS one
BACKGROUND: After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Alth...

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.

International journal of molecular sciences
Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation...