AIMC Topic: Gene Expression Profiling

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Exploring the comorbidity mechanisms of ITGB2 in rheumatoid arthritis and membranous nephropathy through integrated bioinformatics analysis.

Renal failure
BACKGROUND: Patients with rheumatoid arthritis (RA) are more likely to comorbid renal diseases, with membranous nephropathy (MN) being the most common. This study aimed to explore the common pathogenesis between RA and MN using integrated bioinformat...

Histology image analysis of 13 healthy tissues reveals molecular-histological correlations.

Scientific reports
Gene expression is an important process in which genes guide the synthesis of proteins, and molecular-level differences often lead to individual phenotypic variations. Combining molecular information at the nano-level with phenotypic information at t...

An autoencoder learning method for predicting breast cancer subtypes.

PloS one
Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizi...

Integrating transcriptomics, network analysis, and single-cell RNA sequencing to identify and validate key target genes of gynostemma in the treatment of non-alcoholic fatty liver disease.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
This study explores the therapeutic targets and mechanisms of Gynostemma pentaphyllum in non-alcoholic fatty liver disease (NAFLD). Using network analysis and bioinformatics, we identified target genes of Gynostemma's active metabolites in NAFLD thro...

Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma.

PloS one
Chloride channel accessory 1 (CLCA1) is considered a potential prognostic biomarker for colon adenocarcinoma (COAD). The objective of this research was to develop two pathomics models to predict CLCA1 expression from hematoxylin-eosin (H&E) stained p...

Exploration of common pathogenic genes between cerebral amyloid angiopathy and insomnia based on bioinformatics and experimental validation.

Scientific reports
Cerebral amyloid angiopathy (CAA) and insomnia are age-related neurological disorders increasingly recognized as being closely associated. However, research on the shared genes and their biological mechanisms remains limited. This study aims to ident...

Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach.

Scientific reports
Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress of AMI involves immune cell infiltration. However, suitable clinical diagnostic biomarkers and the roles of immune cells in AMI remain unknown. Three ...

Prediction of hub genes in pulpal inflammation and regeneration using autoencoders and a generative AI approach.

Scientific reports
Pulpal inflammation and regeneration are crucial for enhancing endodontic treatment outcomes. Transcriptomic studies highlight the involvement of proinflammatory cytokines, NF-κB signaling, and stem cell activity. This study employs a generative AI a...

STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition.

Genome biology
Recent advances in spatial transcriptomics technologies have enabled gene expression profiling across the transcriptome in spots with subcellular resolution, but high sparsity and dimensionality present significant computational challenges. We presen...

Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment.

Scientific reports
DNA metabolism genes play pivotal roles in the regulation of cellular processes that contribute to cancer progression, immune modulation, and therapeutic response in prostate cancer (PC). Understanding the mechanisms by which these genes influence th...