AIMC Topic: MicroRNAs
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Graph neural networks for the identification of novel inhibitors of a small RNA.
SLAS discovery : advancing life sciences R & D
Oct 14, 2023
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
Oct 9, 2023
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
Oct 3, 2023
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
Sep 13, 2023
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
Sep 7, 2023
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
Jul 14, 2023
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
Jun 9, 2023
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
Jun 9, 2023
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...
An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based on Artificial Gorilla Troops Algorithm.
Sensors (Basel, Switzerland)
Feb 16, 2023
MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target s...
Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics.
Contrast media & molecular imaging
Feb 5, 2023
BACKGROUND: Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify ...
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