AIMC Topic: Transcriptome

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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...
Gene Expression Profiling Transcriptome ROC Curve Brain Neoplasms Male Middle Aged Lymphocytes, Tumor-Infiltrating Cell Death Myeloid Differentiation Factor 88 Gene Expression Regulation, Neoplastic Nomograms Glioma Humans Machine Learning Prognosis Female Biomarkers, Tumor
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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...
Algorithms Gene Expression Profiling Transcriptome RNA-Seq Computational Biology Alzheimer Disease Humans Machine Learning
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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...
Humans Adenosine Machine Learning Sequence Analysis, RNA Transcriptome Methylation RNA, Messenger Calibration
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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 Non-alcoholic Fatty Liver Disease Gene Expression Profiling Transcriptome Humans Support Vector Machine Machine Learning Algorithms
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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...
In Situ Hybridization, Fluorescence Single Molecule Imaging Humans Software Single-Cell Analysis Animals Image Processing, Computer-Assisted Gene Expression Profiling Supervised Machine Learning Transcriptome Deep Learning
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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...
Gene Expression Profiling Transcriptome Deep Learning Animals Male Molecular Docking Simulation Disease Models, Animal Subarachnoid Hemorrhage Single-Cell Analysis Rats Rats, Sprague-Dawley Neural Networks, Computer Machine Learning Macrophages Biomarkers
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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 RNA, Plant Plastids Transcriptome Deep Learning 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...
Quantitative Trait Loci Polymorphism, Single Nucleotide Cholesterol Deep Learning Humans Genome-Wide Association Study Phenotype Genetic Predisposition to Disease Transcriptome
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Deep-learning methods for unveiling large-scale single-cell transcriptomes.

Cancer biology & medicine Feb 5, 2024
Humans Deep Learning Transcriptome
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Deep centroid: a general deep cascade classifier for biomedical omics data classification.

Bioinformatics (Oxford, England) Feb 1, 2024
MOTIVATION: Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent bia...
Precision Medicine Humans Neoplasms Transcriptome Machine Learning DNA Methylation Cell-Free Nucleic Acids Early Detection of Cancer
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