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MicroRNAs

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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.

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies.

Journal of biomolecular structure & dynamics
Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS...

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations.

Computers in biology and medicine
MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' b...

Validation of a Salivary miRNA Signature of Endometriosis - Interim Data.

NEJM evidence
BACKGROUND: The discovery of a saliva-based micro–ribonucleic acid (miRNA) signature for endometriosis in 2022 opened up new perspectives for early and noninvasive diagnosis of the disease. The 109-miRNA saliva signature is the product of miRNA bioma...