AIMC Topic: Gene Expression Profiling

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Identification and validation of oxidative stress-related genes in biliary atresia.

Pediatric surgery international
PURPOSE: Increasing evidence has indicated a role of oxidative stress in the pathogenesis of biliary atresia (BA). This study aimed to identify key oxidative stress-related biomarkers in BA and explore their therapeutic potential.

Identifying SUMOylation-related genes in liver fibrosis with bioinformatics and experimental models for diagnostic insights.

Scientific reports
Liver fibrosis (LF) is a medical disorder caused by prolonged chronic liver injury, which, if left untreated, can progress to cirrhosis or liver cancer, posing significant risks to patient health. In recent years, the increase in liver diseases, incl...

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...

DCN, NPM3 and SULF1 are hub genes related to vasculogenic mimicry in lung adenocarcinoma.

Journal of cancer research and clinical oncology
AIM: Vasculogenic mimicry (VM), a process in which cancer cells form endothelial cell-independent vascular networks, is a hallmark of tumor aggressiveness in lung adenocarcinoma (LUAD) and supports tumor growth and metastasis. This study aims to iden...

TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data.

BMC bioinformatics
BACKGROUND: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of...

Integrating multi-omics and machine learning to decipher the role of GSTP1 in endocrine-disrupting chemical-induced prostate cancer pathogenesis.

European journal of pharmacology
Prostate cancer (PCa) pathogenesis involves complex interactions between genetic susceptibility and exposure to endocrine-disrupting chemicals (EDCs). This study aimed to systematically identify key genes linking EDC exposure to PCa using an integrat...

MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics.

Genome biology
Recent advances in spatial transcriptomics (ST) highlight the need to integrate multiple slices for joint analysis. A key challenge is generating interpretable embeddings that preserve spatial geometry while correcting batch effects. We present MaskG...

A joint complex network and machine learning approach for the identification of discriminative gene communities in autistic brain.

PloS one
Autism is a genetically and clinically very heterogeneous group of disorders. Gene co-expression network analysis can help unravel its complex genetic architecture through the identification of communities of genes that are dysregulated. Using a publ...

Development of a diagnostic model for ovarian cancer based on machine learning algorithms and functional analysis of key biomarker SOX17.

Journal of ovarian research
BACKGROUND: Ovarian cancer (OC) demonstrates the poorest prognosis among gynecological malignancies, with five-year survival rates below 45%, primarily due to late-stage diagnosis. To address this challenge, we systematically identified OC-specific d...