AIMC Topic: Transcriptome

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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

BMC cancer Apr 9, 2025
OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise i...
Graph Neural Networks Computational Biology Immunotherapy Gene Expression Regulation, Neoplastic Humans Neural Networks, Computer Tumor Microenvironment Prognosis Biomarkers, Tumor Melanoma Gene Expression Profiling Skin Neoplasms Transcriptome
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Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.

JCI insight Apr 8, 2025
Using transcriptomic profiling at single-cell resolution, we investigated cell-intrinsic and cell-extrinsic signatures associated with pathogenesis and inflammation-driven fibrosis in both adult and pediatric patients with localized scleroderma (LS)....
RNA-Seq Scleroderma, Systemic Scleroderma, Localized Adolescent Child Single-Cell Analysis Female Adult Humans Male Middle Aged Machine Learning Gene Expression Profiling Transcriptome Case-Control Studies
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Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.

BMC cancer Apr 7, 2025
BACKGROUND: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melan...
Female Melanoma Skin Neoplasms Tumor Microenvironment Biomarkers, Tumor Gene Expression Profiling Transcriptome Humans Machine Learning Prognosis Gene Expression Regulation, Neoplastic Cell Line, Tumor Mutation Immunotherapy Multiomics
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Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.

Scientific reports Apr 5, 2025
Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to its diverse clinical manifestations and underlying biological complexity. This study explored machine learning approaches to enhance ASD screening acc...
Child Cluster Analysis Autistic Disorder Machine Learning Phenotype Autism Spectrum Disorder Female Male Gene Expression Profiling Transcriptome Humans
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MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.

Science advances Apr 4, 2025
Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and ...
Humans COVID-19 SARS-CoV-2 T-Lymphocytes Single-Cell Analysis Lung Neoplasms Receptors, Antigen, T-Cell Immunotherapy Gene Expression Profiling Transcriptome Deep Learning CD8-Positive T-Lymphocytes Chemokine CXCL13
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Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research.

Cancer letters Apr 3, 2025
In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification o...
Humans Tumor Microenvironment Biomarkers, Tumor Single-Cell Analysis Neoplasms RNA-Seq Gene Expression Profiling Computational Biology Transcriptome Gene Expression Regulation, Neoplastic
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5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression.

Neuromolecular medicine Apr 3, 2025
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to...
Muscle, Skeletal Drug Repositioning Amyotrophic Lateral Sclerosis Machine Learning Humans Motor Neurons Gene Expression Profiling Transcriptome Gene Regulatory Networks
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Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning.

PLoS computational biology Apr 3, 2025
Due to the rapid development of spatial sequencing technologies, large amounts of spatial transcriptomic datasets have been generated across various technological platforms or different biological conditions (e.g., control vs. treatment). Spatial tra...
Databases, Genetic Gene Expression Profiling Transcriptome Computational Biology Humans Machine Learning Algorithms
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Causal machine learning for single-cell genomics.

Nature genetics Mar 31, 2025
Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transc...
Single-Cell Analysis Humans Genomics Animals Machine Learning Gene Expression Profiling Transcriptome
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Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.

Frontiers in immunology Mar 28, 2025
BACKGROUND: Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associate...
Mice Drug Resistance, Neoplasm Gene Expression Regulation, Neoplastic Animals Humans T-Lymphocytes, Regulatory STAT1 Transcription Factor Gene Expression Profiling Machine Learning Melanoma Transcriptome Immune Checkpoint Inhibitors
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