AIMC Topic: Transcriptome
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Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy.
Scientific reports
Apr 29, 2025
This study looked at possible targets for hypertrophic cardiomyopathy (HCM), a condition marked by thickening of the ventricular wall, primarily in the left ventricle. We employed differential gene analysis and weighted gene co-expression network ana...
Identification and Validation of Glycosylation‑Related Genes in Ischemic Stroke Based on Bioinformatics and Machine Learning.
Journal of molecular neuroscience : MN
Apr 29, 2025
Ischemic stroke (IS) constitutes a severe neurological disorder with restricted treatment alternatives. Recent investigations have disclosed that glycosylation is closely associated with the occurrence and outcome of IS. Nevertheless, data on the tra...
A novel cancer-associated fibroblast-related gene signature for predicting diffuse large B cell lymphoma prognosis using weighted gene co-expression network analysis and machine learning.
The Journal of international medical research
Apr 25, 2025
ObjectiveOur objective was to investigate a novel cancer-associated fibroblast-related gene signature for predicting clinical outcomes in patients with diffuse large B cell lymphoma.MethodsThe cancer-associated fibroblast-related module genes were id...
Transcriptome
ROC Curve
Gene Regulatory Networks
Humans
Gene Expression Regulation, Neoplastic
Machine Learning
Nomograms
Lymphoma, Large B-Cell, Diffuse
Cancer-Associated Fibroblasts
Prognosis
Male
Kaplan-Meier Estimate
Retrospective Studies
Middle Aged
Female
Gene Expression Profiling
Biomarkers, Tumor
Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.
Scientific reports
Apr 24, 2025
Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease characterized by elevated pulmonary arterial pressure, leading to right heart failure and death. Despite advancements in diagnosis and treatment, it remains incurable, and ...
Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.
Scientific reports
Apr 24, 2025
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...
Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning.
Scientific reports
Apr 24, 2025
Subarachnoid hemorrhage (SAH) is a disease with high mortality and morbidity, and its pathophysiology is complex but poorly understood. To investigate the potential therapeutic targets post-SAH, the SAH-related feature genes were screened by the comb...
Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research.
PloS one
Apr 24, 2025
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. T...
An integrated machine learning model of transcriptomic genes in multi-center chronic obstructive pulmonary disease reveals the causal role of TIMP4 in airway epithelial cell.
Respiratory research
Apr 23, 2025
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome, resulting in inconsistent findings across studies. Identifying a core set of genes consistently involved in COPD pathogenesis, independent of patient variability, i...
The inconsistent pathogenesis of endometriosis and adenomyosis: insights from endometrial metabolome and microbiome.
mSystems
Apr 22, 2025
UNLABELLED: Endometriosis (EM) and adenomyosis (AM) are interrelated gynecological disorders characterized by the aberrant presence of endometrial tissue and are frequently linked with chronic pelvic pain and infertility, yet their pathogenetic mecha...
Deconvolution of cell types and states in spatial multiomics utilizing TACIT.
Nature communications
Apr 21, 2025
Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in...
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