AIMC Topic: RNA-Seq

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Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

International journal of molecular sciences May 29, 2024
A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pat...
Biomarkers SARS-CoV-2 Gene Expression Profiling Humans Machine Learning COVID-19 RNA-Seq Single-Cell Gene Expression Analysis Sequence Analysis, RNA Transcriptome Sepsis Single-Cell Analysis
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A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.

STAR protocols May 14, 2024
Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capt...
Humans RNA-Seq Deep Learning Single-Cell Gene Expression Analysis Single-Cell Analysis Sequence Analysis, RNA
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Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

Frontiers in endocrinology May 10, 2024
BACKGROUND: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and A...
Sequence Analysis, RNA Female Protein Interaction Maps Case-Control Studies Humans Male Oxidative Stress RNA-Seq Diabetic Retinopathy Molecular Docking Simulation Single-Cell Analysis Single-Cell Gene Expression Analysis Machine Learning
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A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

BMC bioinformatics May 8, 2024
BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene ex...
Computational Biology Sequence Analysis, RNA Humans Machine Learning Neoplasms Gene Expression Profiling Transcriptome RNA-Seq
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Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Genetics research Apr 30, 2024
Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosi...
Aged Radiomics Biomarkers, Tumor Male Middle Aged Transcriptome Prognosis Tomography, X-Ray Computed Female Humans Neural Networks, Computer Urinary Bladder Neoplasms ROC Curve Neoplasm Staging RNA-Seq Nomograms
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Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation.

Journal of biomedical informatics Apr 15, 2024
OBJECTIVE: Gene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of gene regulation in diverse cell types, tissues, and organisms. While existing methods primarily focus on identifying cell type-...
Gene Expression Profiling RNA-Seq Single-Cell Gene Expression Analysis Mice Computational Biology Dentate Gyrus Algorithms Animals Colon Humans Machine Learning
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The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.

Andrology Apr 5, 2024
BACKGROUND: Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines...
Adult Male Single-Cell Analysis Humans Testis Infertility, Male Spermatogenesis RNA-Seq Single-Cell Gene Expression Analysis Sequence Analysis, RNA
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PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.

Cell reports methods Feb 6, 2024
Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined anno...
Algorithms Deep Learning Humans RNA-Seq Sequence Analysis, RNA RNA Polyadenylation
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MRI/RNA-Seq-Based Radiogenomics and Artificial Intelligence for More Accurate Staging of Muscle-Invasive Bladder Cancer.

International journal of molecular sciences Dec 20, 2023
Accurate staging of bladder cancer assists in identifying optimal treatment (e.g., transurethral resection vs. radical cystectomy vs. bladder preservation). However, currently, about one-third of patients are over-staged and one-third are under-stage...
Muscles Humans Artificial Intelligence RNA-Seq Magnetic Resonance Imaging Urinary Bladder Neoplasms
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Generating bulk RNA-Seq gene expression data based on generative deep learning models and utilizing it for data augmentation.

Computers in biology and medicine Dec 7, 2023
Large-scale high-throughput transcriptome sequencing data holds significant value in biomedical research. However, practical challenges such as difficulty in sample acquisition often limit the availability of large sample sizes, leading to decreased ...
Reproducibility of Results Transcriptome Deep Learning RNA-Seq High-Throughput Nucleotide Sequencing
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