AIMC Topic: Transcriptome

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Machine learning identifies inflammation-related diagnostic biomarkers for primary myelofibrosis with clinical validation.

Scientific reports
Primary myelofibrosis (PMF) is a heterogeneous bone marrow disorder, and substantial evidence indicates the involvement of inflammatory mediators in its progression. However, a diagnostic model based on inflammation-related genes has not yet been est...

The role of IRF-1 in mediating T-cell immune imbalance in systemic lupus erythematosus and the construction of a diagnostic model.

Autoimmunity
Systemic lupus erythematosus (SLE), characterized by immune dysregulation, urgently requires improved diagnostic tools and mechanistic insights. The role of interferon regulatory factor-1 (IRF-1) remains unclear. We integrated single-cell transcripto...

Construction and validation of gene signature for prognosis and drug sensitivity in cholangiocarcinoma based on cellular senescence related genes.

Scientific reports
Cholangiocarcinoma is a very deadly epithelial cell cancer with poor clinical outcome. Cellular senescence plays a vital role in the oncogenesis and the aggressiveness of cholangiocarcinoma. Integrative machine learning procedure including 10 methods...

Biologically explainable multi-omics feature demonstrates greater learning potential by identifying tissue of origin, stages, and subtypes for pan-cancer classification.

Scientific reports
Cancer is a complex disease characterized by uncontrolled cell growth, which can invade surrounding tissues and spread to distant organs. Most of the conventional methods of diagnosis fails to identify the primary organ when cancer spreads to other o...

Integrated machine learning and single-cell analysis identify chromatin-remodeling gene signature for diagnosis and prognosis in nasopharyngeal carcinoma.

Clinical and experimental medicine
This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple data...

Multi-omics unravel heterogeneity of glucose metabolism reprogramming in gastric cancer.

Clinical and experimental medicine
Gastric cancer (GC) presents striking survival disparities: 85-100% for early-stage versus only 5-20% for advanced disease. Glucose metabolic reprogramming (GMS)-a cancer hallmark linked to the Warburg effect-fuels tumor progression and immune evasio...

SpatialFusion: A Unified Model for Integrating Spatial Transcriptomics to Unveil Cell-type Distribution, Interaction, and Functional Heterogeneity in Tissue Microenvironments.

Journal of molecular biology
Recent advances in spatial transcriptomics (ST) have significantly enhanced our understanding of tissue structure and intercellular interactions. However, existing methods for spatial domain identification and cell type deconvolution still face chall...

Integrating machine learning and experimental validation identifies a post-translational modification gene signature for prognosis and treatment response in breast cancer.

Scientific reports
Breast cancer (BC) is the most prevalent malignancy among women, and the steadily increasing disease burden has garnered considerable global attention. Post-translational modifications (PTMs) are critical in the initiation and progression of BC. This...

Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models.

Scientific reports
Acute Myeloid Leukemia (AML) is a genetically and clinically heterogeneous disease that can develop at any age. While AML incidence increases with age and distinct genetic alterations are observed in younger versus older patients, current classificat...

SpaMWGDA: Identifying spatial domains of spatial transcriptomes using multi-view weighted fusion graph convolutional network and data augmentation.

PLoS computational biology
The rapid development of spatial transcriptomics (ST) has made it possible to effectively integrate gene expression and spatial information of cells and accurately identify spatial domains. A large number of deep learning (DL)-based methods have been...