AIMC Topic: MicroRNAs

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Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.

RNA biology
Cancer diagnosis at an early stage is crucial for improving overall health outcomes. However, existing cancer diagnostic techniques are mostly invasive and tend to identify the disease only in its advanced stages. MicroRNAs (miRNAs), which are small ...

Identification of key biomarkers associated with necroptosis and immune infiltration in hepatitis B virus-related acute-on-chronic liver failure.

Scientific reports
Hepatitis B virus-related acute-on-chronic liver failure (ACHBLF) is a severe condition associated with short-term mortality without liver transplantation. Substantial evidence indicates that necroptosis and immune infiltration play critical roles in...

Plasma multi-omics and machine learning reveal predictive biomarkers for type 2 diabetes and retinopathy in Qatar biobank cohort.

Journal of translational medicine
BACKGROUND: Type 2 diabetes (T2D) and its vascular complications, including diabetic retinopathy (DR), are escalating in prevalence globally, with disproportionately high prevalence in Middle Eastern populations, where genetic predispositions and lif...

Machine learning and multi-omics integration reveal TRPV2 as a central regulator in bicuspid aortic valve calcification.

Biochemical and biophysical research communications
BACKGROUND: Bicuspid aortic valve (BAV), the most common congenital heart defect, is strongly predisposed to early calcification, yet the molecular drivers remain poorly defined. This study aims to identify the functional role of transient receptor p...

Machine learning identifies MiRNA biomarkers and immune mechanisms in active tuberculosis.

Scientific reports
Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global public health threat. The rising prevalence of HIV/TB co-infection and multidrug-resistant tuberculosis (MDR-TB) has further intensified this challenge. This study ...

Machine Learning-Enhanced Analysis of miRNA Biomarkers for Accurate Breast Cancer Diagnosis Using DNA Seagrass.

Analytical chemistry
As potential biomarkers for breast cancer, microRNAs (miRNAs) have demonstrated significant promise in clinical applications. However, accurate miRNA-based breast cancer diagnosis is hindered by the lack of simple, ultrasensitive, and highly specific...

Foundation model based multimodal transformer framework for survival analysis in HER2 stratified breast cancer.

Physics in medicine and biology
. To improve survival prediction for HER2-positive breast cancer by integrating histopathological, molecular, and clinical data using a multimodal transformer framework.. We propose a multimodal transformer framework for breast cancer survival predic...

DeepRNA-Reg: a deep-learning based approach for comparative analysis of CLIP experiments.

RNA biology
DeepRNA-Reg employs advances in deep learning to enable high-fidelity comparative analysis of paired datasets of high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). In a HITS-CLIP experimental paradigm where Ag...

Machine learning-based identification of small RNA signatures in aqueous humor as a step toward precision diagnosis of glaucoma.

Annals of medicine
BACKGROUND: Glaucoma is a progressive neurodegenerative disease of the optic nerve and one of the leading causes of irreversible blindness worldwide. Small RNAs (including miRNAs) play an important role in the pathogenesis of the disease. Despite ext...

Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.

Scientific reports
This study aimed to identify candidate diagnostic miRNAs from the serum of colorectal cancer (CRC) patients using Boruta, a wrapper-based feature selection technique, in combination with decision tree-based machine learning methods. We analyzed three...