MicroRNAs

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

Briefings in bioinformatics 39656775

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...

Algorithms Biomarkers, Tumor Breast Neoplasms Computational Biology Databases, Genetic Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning MicroRNAs Neoplasm Staging

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MicroRNA signature for early prediction of knee osteoarthritis structural progression using integrated machine and deep learning approaches.

Osteoarthritis and cartilage 39617204

OBJECTIVE: Conventional methodologies are ineffective in predicting the rapid progression of knee osteoarthritis (OA). MicroRNAs (miRNAs) show promise as biomarkers for patient stratification. We aimed to develop a miRNA prognosis model for identifyi...

Aged Biomarkers Deep Learning Disease Progression Female Humans Machine Learning Magnetic Resonance Imaging Male MicroRNAs Middle Aged Osteoarthritis, Knee Prognosis

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CLHGNNMDA: Hypergraph Neural Network Model Enhanced by Contrastive Learning for miRNA-Disease Association Prediction.

Journal of computational biology : a journal of computational molecular cell biology 39602201

Numerous biological experiments have demonstrated that microRNA (miRNA) is involved in gene regulation within cells, and mutations and abnormal expression of miRNA can cause a myriad of intricate diseases. Forecasting the association between miRNA an...

Algorithms Computational Biology Genetic Predisposition to Disease Humans Machine Learning MicroRNAs Neural Networks, Computer

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

Briefings in bioinformatics 39592153

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...

Algorithms Computational Biology Deep Learning Humans MicroRNAs Neural Networks, Computer RNA, Messenger

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A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification.

Forensic science international. Genetics 39591840

MicroRNAs (miRNAs) are promising biomarkers for forensic body fluid identification owing to their small size, stability against degradation, and differential expression patterns. However, the expression of most body fluid-miRNAs is relative (differen...

Adult Body Fluids Cervix Mucus Female Forensic Genetics Gene Expression Profiling Genetic Markers Humans Male Menstruation MicroRNAs Pericardial Fluid Real-Time Polymerase Chain Reaction Saliva Semen Support Vector Machine

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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway.

BMC cancer 39574053

BACKGROUND: Exosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated th...

Adult Biomarkers, Tumor Breast Neoplasms Chemokines Early Detection of Cancer Exosomes Female Gene Expression Regulation, Neoplastic Humans Machine Learning MicroRNAs Middle Aged ROC Curve Signal Transduction

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Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.

Journal of assisted reproduction and genetics 39540991

PURPOSE: Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics meth...

Cellular Senescence Computational Biology Female Gene Expression Profiling Gene Regulatory Networks Humans Machine Learning MicroRNAs Oocytes RNA, Messenger Transcription Factors

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An exploration into the diagnostic capabilities of microRNAs for myocardial infarction using machine learning.

Biology direct 39658789

BACKGROUND: MicroRNAs (miRNAs) have shown potential as diagnostic biomarkers for myocardial infarction (MI) due to their early dysregulation and stability in circulation after MI. Moreover, they play a crucial role in regulating adaptive and maladapt...

Biomarkers Computational Biology Gene Expression Profiling Humans Machine Learning MicroRNAs Myocardial Infarction Support Vector Machine

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GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.

Journal of cellular and molecular medicine 39628446

Multimodal information provides valuable resources for cancer prognosis and survival prediction. However, the computational integration of this heterogeneous data information poses significant challenges due to the complex interactions between molecu...

Algorithms Biomarkers, Tumor Computational Biology Deep Learning DNA Methylation Gene Expression Regulation, Neoplastic Humans Liver Neoplasms MicroRNAs Neoplasms Prognosis

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miRStart 2.0: enhancing miRNA regulatory insights through deep learning-based TSS identification.

Nucleic acids research 39578697

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to the 3'-untranslated regions of target mRNAs, influencing various biological processes at the post-transcriptional level. Identifying miRNA transcription start si...

Animals Deep Learning Gene Expression Regulation Humans Mice MicroRNAs Software Transcription Factors Transcription Initiation Site

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

MicroRNA signature for early prediction of knee osteoarthritis structural progression using integrated machine and deep learning approaches.

CLHGNNMDA: Hypergraph Neural Network Model Enhanced by Contrastive Learning for miRNA-Disease Association Prediction.

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

A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification.

A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway.

Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.

An exploration into the diagnostic capabilities of microRNAs for myocardial infarction using machine learning.

GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.

miRStart 2.0: enhancing miRNA regulatory insights through deep learning-based TSS identification.

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