AIMC Topic: Transcriptome

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[Identification of oxidative stress-related biomarkers in chronic rhinosinusitis with nasal polyps using WGCNA combined with machine learning algorithms].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Jun 7, 2024
To identify diagnostic markers related to oxidative stress in chronic rhinosinusitis with nasal polyps (CRSwNP) by analyzing transcriptome sequencing data, and to investigate their roles in CRSwNP. Utilizing four CRSwNP sequencing datasets, differe...
Biomarkers Gene Expression Profiling Transcriptome Humans Machine Learning Algorithms Chronic Disease Rhinosinusitis Oxidative Stress Gene Regulatory Networks Nasal Polyps
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Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

Experimental dermatology Jun 1, 2024
This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE2...
Humans Machine Learning Single-Cell Analysis Mutation Melanoma, Cutaneous Malignant Lactic Acid Biomarkers, Tumor Transcriptome Cell Line, Tumor Prognosis Melanoma Skin Neoplasms
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Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma.

Journal of cellular and molecular medicine Jun 1, 2024
Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO...
Male Middle Aged Gene Expression Profiling Transcriptome Glioma Female ROC Curve Cell Death Machine Learning Lymphocytes, Tumor-Infiltrating Humans Biomarkers, Tumor Gene Expression Regulation, Neoplastic Myeloid Differentiation Factor 88 Prognosis Brain Neoplasms Nomograms
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AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Briefings in bioinformatics May 23, 2024
Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-seq...
Machine Learning Algorithms Alzheimer Disease Gene Expression Profiling Transcriptome Humans RNA-Seq Computational Biology
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m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.

Nucleic acids research May 22, 2024
We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a g...
Machine Learning Transcriptome RNA, Messenger Calibration Adenosine Sequence Analysis, RNA Methylation Humans
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Identification of disease-specific genes related to immune infiltration in nonalcoholic steatohepatitis using machine learning algorithms.

Medicine May 17, 2024
To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gene expression profiles, GSE164760 and GSE37031, from the gene expression omnibus database. These prof...
Hep G2 Cells Humans Gene Expression Profiling Non-alcoholic Fatty Liver Disease Machine Learning Transcriptome Algorithms Support Vector Machine
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Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Cell systems May 15, 2024
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial tran...
Software Animals Gene Expression Profiling Single-Cell Analysis Image Processing, Computer-Assisted Transcriptome Deep Learning Humans Supervised Machine Learning In Situ Hybridization, Fluorescence Single Molecule Imaging
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Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.

Journal of cellular and molecular medicine May 1, 2024
We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcript...
Disease Models, Animal Subarachnoid Hemorrhage Single-Cell Analysis Rats Rats, Sprague-Dawley Biomarkers Animals Male Transcriptome Deep Learning Neural Networks, Computer Molecular Docking Simulation Machine Learning Gene Expression Profiling Macrophages
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PlantC2U: deep learning of cross-species sequence landscapes predicts plastid C-to-U RNA editing in plants.

Journal of experimental botany Apr 15, 2024
In plants, C-to-U RNA editing mainly occurs in plastid and mitochondrial transcripts, which contributes to a complex transcriptional regulatory network. More evidence reveals that RNA editing plays critical roles in plant growth and development. Howe...
RNA Editing Transcriptome RNA, Plant Deep Learning Plastids Plants
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DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.

Biostatistics (Oxford, England) Apr 15, 2024
Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regre...
Cholesterol Humans Quantitative Trait Loci Phenotype Genetic Predisposition to Disease Transcriptome Deep Learning Polymorphism, Single Nucleotide Genome-Wide Association Study
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