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Biomarkers

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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...

Handwriting strokes as biomarkers for Alzheimer's disease prediction: A novel machine learning approach.

Computers in biology and medicine
In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supporting the early diagnosis of Alzheimer's disease and predicting its progression. Traditional approaches represent handwriting tasks using a single fe...

A quantum inspired machine learning approach for multimodal Parkinson's disease screening.

Scientific reports
Parkinson's disease, currently the fastest-growing neurodegenerative disorder globally, has seen a 50% increase in cases within just two years. As disease progression impairs speech, memory, and motor functions over time, early diagnosis is crucial f...

Data independent acquisition proteomics and machine learning reveals that proteins associated with immunity are potential molecular markers for early diagnosis of autism.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Early diagnosis of autism is critical to its treatment, but so far, there is no clear molecular marker for early diagnosis in children.

Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

Cardiovascular diabetology
BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major advers...

Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning.

Scientific reports
Psoriasis is a common chronic skin disorder with a polygenic background. It is widely acknowledged that Th17/IL-17A axis plays a key role in the pathogenesis of psoriasis. However, numerous regulatory genes upstream of the pathway remain undiscovered...

Identification of biomarkers related to iron death in diabetic kidney disease based on machine learning algorithms.

Annals of human biology
BACKGROUND: While ferroptosis has been recognised for its key role in tumour development, its involvement in DKD is not well understood. Identifying differentially expressed ferroptosis-related genes (DEIRGs) could help improve early diagnosis and tr...

Towards a unified framework for single-cell -omics-based disease prediction through AI.

Clinical and translational medicine
Single-cell omics has emerged as a powerful tool for elucidating cellular heterogeneity in health and disease. Parallel advances in artificial intelligence (AI), particularly in pattern recognition, feature extraction and predictive modelling, now of...

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to the accumulation of more...