AIMC Topic: MicroRNAs

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Influences of microRNA-451 on the expression of HMGB1 in myocardial cells and its mechanism in ischemia-reperfusion injury.

Cellular and molecular biology (Noisy-le-Grand, France)
This work aimed to understand the underlying mechanism of micro-ribonucleic acid (MicroRNA) (miR)-451 in ischemia-reperfusion injury (IRI) and the influences of miR-451 on high mobility group box 1 protein (HMGB1) in myocardial cells, 30 specific pat...

A Machine Learning Approach for Highlighting microRNAs as Biomarkers Linked to Amyotrophic Lateral Sclerosis Diagnosis and Progression.

Biomolecules
Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons in the brain and spinal cord. The early diagnosis of ALS can be challenging, as it usually depends on clinical examination...

The effect of thalidomide on the invasive ability of gastric cancer cells by regulating miR-524-5p/FSTL1.

Cellular and molecular biology (Noisy-le-Grand, France)
This study aimed to investigate the effect of thalidomide (Thal) regulating microRNA (miR)-524-5p/follistatin-like protein 1 (FSTL1) on the invasion ability of gastric cancer cells. For this purpose, real-time fluorescent quantitative PCR (RT-qPCR) w...

Hessian Regularized -Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Interdisciplinary sciences, computational life sciences
Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate...

Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning.

Biosensors & bioelectronics
False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge ...

Graph neural networks for the identification of novel inhibitors of a small RNA.

SLAS discovery : advancing life sciences R & D
MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzin...

NGCICM: A Novel Deep Learning-Based Method for Predicting circRNA-miRNA Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
The circRNAs and miRNAs play an important role in the development of human diseases, and they can be widely used as biomarkers of diseases for disease diagnosis. In particular, circRNAs can act as sponge adsorbers for miRNAs and act together in certa...

Predicting Plant miRNA-lncRNA Interactions via a Deep Learning Method.

IEEE transactions on nanobioscience
In recent years, due to the contribution to elucidating the functional mechanisms of miRNAs and lncRNAs, the research on miRNA-lncRNA interaction prediction has increased exponentially. However, the prediction research is challenging in bioinformatic...

Peripheral blood MicroRNAs as biomarkers of schizophrenia: expectations from a meta-analysis that combines deep learning methods.

The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
OBJECTIVES: This study aimed at identifying reliable differentially expressed miRNAs (DEMs) for schizophrenia in blood meta-analyses combined with deep learning methods.

A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.

Journal of cancer research and clinical oncology
PURPOSE: Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM.