AIMC Topic: Transcriptome
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Identification of fatty acid metabolism signature genes in patients with pulmonary arterial hypertension using WGCNA and machine learning.
The Journal of international medical research
Sep 1, 2024
OBJECTIVE: To investigate the signature genes of fatty acid metabolism and their association with immune cells in pulmonary arterial hypertension (PAH).
Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs.
Bioinformatics (Oxford, England)
Sep 1, 2024
MOTIVATION: Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologie...
Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.
Journal of cellular and molecular medicine
Aug 1, 2024
Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating ...
BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.
Briefings in bioinformatics
Jul 25, 2024
Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratificati...
Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.
Journal of cellular and molecular medicine
Jul 1, 2024
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intellig...
SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.
Bioinformatics (Oxford, England)
Jul 1, 2024
MOTIVATION: The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, techn...
DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.
Bioinformatics (Oxford, England)
Jul 1, 2024
MOTIVATION: Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequenci...
Adaptive digital tissue deconvolution.
Bioinformatics (Oxford, England)
Jun 28, 2024
MOTIVATION: The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systema...
[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...
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...
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