AIMC Topic: Biomarkers

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ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

Mikrochimica acta
The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in a dynamic concentration range. Traditionally, the saturation of sensor response limits accurate pre...

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.

Neurology
BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We...

Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study.

Scientific reports
Preterm birth (PTB), defined as delivery before 37 weeks, affects 15 million infants annually, accounting for 11% of live births and over 35% of neonatal deaths. While advanced maternal age (≥ 35 years) is a known risk factor, PTB risk in women under...

An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test.

Asian journal of psychiatry
BACKGROUND: Individuals with anxious-depressed (AD) symptoms have more severe mood disorders and cognitive impairment than those with non-anxious depression (NAD) symptoms. Therefore, it is important to clarify the underlying neurophysiology of these...

Bioinformatics and machine learning approaches to explore key biomarkers in muscle aging linked to adipogenesis.

BMC musculoskeletal disorders
Adipogenesis is intricately linked to the onset and progression of muscle aging; however, the relevant biomarkers remain unclear. This study sought to identify key genes associated with adipogenesis in the context of muscle aging. Firstly, gene expre...

Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning.

European journal of medical research
BACKGROUND: Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.

The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.

Frontiers in public health
INTRODUCTION: Epigenetic biomarkers are molecular indicators of epigenetic changes, and some studies have suggested that these biomarkers have predictive power for disease risk. This study aims to analyze the relationship between 30 epigenetic biomar...

Embracing the changes and challenges with modern early drug discovery.

Expert opinion on drug discovery
INTRODUCTION: The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs are discovered. As traditional drug discovery ...

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.

Scientific reports
Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack of objective pain biomarkers. The present study applies machine learning to predict which patients w...

Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning.

Frontiers in endocrinology
BACKGROUND: Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health problem worldwide. There is evidence that endoplasmic reticulum stress (ERS) is involved in the pathog...