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MicroRNAs

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Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.

Briefings in bioinformatics
The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance the performance of state-of-the-art models. Here, we introduced TEC-LncMir, a novel approach for pr...

microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites.

Briefings in bioinformatics
microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering miRNA targets remains challenging, leading to an incomplete miRNA interactome and partially elucidat...

Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.

Briefings in bioinformatics
Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an ef...

miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA.

Briefings in bioinformatics
MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer a...

A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies.

Bioinformatics (Oxford, England)
MOTIVATION: Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usu...

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

Briefings in bioinformatics
The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding...

THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network.

Briefings in functional genomics
Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of...

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Journal of cellular and molecular medicine
Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mR...

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

The journal of applied laboratory medicine
BACKGROUND: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of micr...