AIMC Topic: RNA-Seq

Clear Filters Showing 31 to 40 of 185 articles

Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation.

Frontiers in immunology Dec 4, 2024
BACKGROUND: Diabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therape...
Phospholipid Transfer Proteins Nomograms Regulated Cell Death Human Umbilical Vein Endothelial Cells Random Forest Gene Expression Regulation Biomarkers Humans ROC Curve Molecular Targeted Therapy CD4-Positive T-Lymphocytes RNA-Seq Memory T Cells Gene Regulatory Networks Fatty Acid Synthase, Type I Diabetic Retinopathy Machine Learning
View on PubMed DOI

The G Protein-Coupled Receptor-Related Gene Signatures for Diagnosis and Prognosis in Glioblastoma: A Deep Learning Model Using RNA-Seq Data.

Asian Pacific journal of cancer prevention : APJCP Dec 1, 2024
BACKGROUND: Glioblastoma (GBM) is the most aggressive cancer in the central nervous system in glial cells. Finding novel biomarkers in GBM offers numerous advantages that can contribute to early detection, personalized treatment, improved patient out...
Glioblastoma Male Middle Aged Gene Expression Profiling Transcriptome Female Deep Learning Humans RNA-Seq Gene Expression Regulation, Neoplastic Survival Rate Receptors, G-Protein-Coupled Biomarkers, Tumor Brain Neoplasms Prognosis
View on PubMed DOI

DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment.

Proceedings of the National Academy of Sciences of the United States of America Nov 8, 2024
Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cytometry and single-cell RNA-seq (scRNA-seq) for analyzing the complex cellular composition of tumor mi...
Gene Expression Regulation, Neoplastic Cell Line, Tumor Single-Cell Analysis RNA-Seq Sequence Analysis, RNA Humans Tumor Microenvironment Neoplasms Gene Expression Profiling Transcriptome Deep Learning
View on PubMed DOI

HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data.

Nature communications Nov 2, 2024
In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in reality only a small fraction of them may actually be perturbed. Here, using simulations, we demonstr...
Humans Machine Learning Blood-Brain Barrier Single-Cell Analysis RNA-Seq Mice Computational Biology Animals Gene Expression Profiling Transcriptome Case-Control Studies Multiple Myeloma
View on PubMed DOI

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.

International journal of biological macromolecules Oct 31, 2024
In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functi...
Animals Male Disease Progression Gene Expression Profiling Humans Machine Learning Single-Cell Gene Expression Analysis Biomarkers Mice Computational Biology Acute Lung Injury Transcriptome Sepsis Single-Cell Analysis RNA-Seq
View on PubMed DOI

Impact of Mitophagy-Related Genes on the Diagnosis and Development of Esophageal Squamous Cell Carcinoma via Single-Cell RNA-seq Analysis and Machine Learning Algorithms.

Journal of microbiology and biotechnology Sep 23, 2024
As a treatment for esophageal squamous cell carcinoma (ESCC), which is common and fatal, mitophagy is a conserved cellular mechanism that selectively removes damaged mitochondria and is crucial for cellular homeostasis. While tumor development and re...
Cell Line, Tumor Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis Gene Expression Regulation, Neoplastic Cell Proliferation Cell Movement Esophageal Neoplasms Algorithms Mitophagy Prognosis Biomarkers, Tumor Humans Transcriptome Mitochondria Machine Learning Esophageal Squamous Cell Carcinoma Apoptosis Regulatory Proteins Mitochondrial Proteins
View on PubMed DOI

Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques.

International journal of molecular sciences Sep 11, 2024
Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the de...
Humans Liver Neoplasms Neural Networks, Computer Carcinoma, Hepatocellular RNA-Seq Deep Learning Hepatitis B virus Sequence Analysis, RNA Hepatitis B Cell Transformation, Neoplastic
View on PubMed DOI

Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq.

Archives of toxicology Sep 6, 2024
Multicollinearity, characterized by significant co-expression patterns among genes, often occurs in high-throughput expression data, potentially impacting the predictive model's reliability. This study examined multicollinearity among closely related...
Gene Expression Regulation, Developmental Embryoid Bodies RNA-Seq Machine Learning Mice Fluorouracil Teratogens Biomarkers Reproducibility of Results Animals
View on PubMed DOI

Occlusion enhanced pan-cancer classification via deep learning.

BMC bioinformatics Aug 8, 2024
Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue ...
RNA-Seq Neural Networks, Computer Biomarkers, Tumor Neoplasms Deep Learning Humans
View on PubMed DOI

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering.

The international journal of biostatistics Jul 29, 2024
A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clus...
Computer Simulation Gene Expression Profiling RNA-Seq Sequence Analysis, RNA Humans Fuzzy Logic Cluster Analysis Sample Size
View on PubMed DOI
Popular Topics
Recent Journals