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MicroRNAs

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Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

Oncology
INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasi...

DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations.

Computational biology and chemistry
Numerous studies have shown that microRNAs (miRNAs) play a key role in human diseases as critical biomarkers. Its abnormal expression is often accompanied by the emergence of specific diseases. Therefore, studying the relationship between miRNAs and ...

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.

Interdisciplinary sciences, computational life sciences
BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into t...

A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations.

Scientific reports
MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treat...

Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

PloS one
MOTIVATION: There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about imp...

TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.

Communications biology
The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge to accurately predict such associations due to improper information encoding. Previous methods char...

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning.

British journal of cancer
BACKGROUND: Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers.

Benchmarking the negatives: Effect of negative data generation on the classification of miRNA-mRNA interactions.

PLoS computational biology
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Comp...

Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model.

Lipids in health and disease
BACKGROUND: Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till ...

Exploration of the shared diagnostic genes and mechanisms between periodontitis and primary Sjögren's syndrome by integrated comprehensive bioinformatics analysis and machine learning.

International immunopharmacology
BACKGROUND: Accumulating evidence has showed a bidirectional link between periodontitis (PD) and primary Sjögren's syndrome (pSS), but the mechanisms of their occurrence remain unclear. Hence, this study aimed to investigate the shared diagnostic gen...