AIMC Topic: RNA-Seq

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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 Prognosis Biomarkers, Tumor Transcriptome Cell Movement Esophageal Neoplasms Mitochondria Single-Cell Gene Expression Analysis Gene Expression Regulation, Neoplastic Cell Proliferation Humans Machine Learning Algorithms Mitophagy Esophageal Squamous Cell Carcinoma Apoptosis Regulatory Proteins Mitochondrial Proteins
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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...
Deep Learning Sequence Analysis, RNA Humans Liver Neoplasms Hepatitis B virus Cell Transformation, Neoplastic Neural Networks, Computer Carcinoma, Hepatocellular Hepatitis B RNA-Seq
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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...
Biomarkers Reproducibility of Results Animals Machine Learning Teratogens Gene Expression Regulation, Developmental Embryoid Bodies RNA-Seq Mice Fluorouracil
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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 ...
Neoplasms Deep Learning RNA-Seq Humans Neural Networks, Computer Biomarkers, Tumor
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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...
Fuzzy Logic Cluster Analysis Sample Size Gene Expression Profiling RNA-Seq Sequence Analysis, RNA Humans Computer Simulation
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Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma.

Genes and immunity Jul 29, 2024
Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to av...
Tumor Microenvironment Biomarkers, Tumor Male RNA-Seq Single-Cell Gene Expression Analysis Lung Neoplasms Machine Learning Humans Adenocarcinoma of Lung Prognosis Female
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Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data.

Scientific reports Jul 24, 2024
Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representatio...
RNA-Seq Computational Biology Genes, Essential Humans Neoplasms Deep Learning
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TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.

PloS one Jul 22, 2024
Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variatio...
Gene Expression Regulation, Neoplastic Deep Learning Humans Databases, Genetic Neoplasms RNA-Seq Gene Expression Profiling Computational Biology
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NNICE: a deep quantile neural network algorithm for expression deconvolution.

Scientific reports Jun 18, 2024
The composition of cell-type is a key indicator of health. Advancements in bulk gene expression data curation, single cell RNA-sequencing technologies, and computational deconvolution approaches offer a new perspective to learn about the composition ...
RNA-Seq Computational Biology Sequence Analysis, RNA Transcriptome Deep Learning Humans Single-Cell Analysis Neural Networks, Computer Algorithms Gene Expression Profiling
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DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.

Scientific reports Jun 12, 2024
Single-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data,...
Sequence Analysis, RNA Cluster Analysis Single-Cell Analysis Humans RNA-Seq Machine Learning Single-Cell Gene Expression Analysis Algorithms
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