AIMC Topic: Gene Expression Profiling

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scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.

Cell genomics
Transcriptome-wide association studies (TWASs) help identify disease-causing genes but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here, we pr...

Classification of lung cancer severity using gene expression data based on deep learning.

BMC medical informatics and decision making
Lung cancer is one of the most prevalent diseases affecting people and is a main factor in the rising death rate. Recently, Machine Learning (ML) and Deep Learning (DL) techniques have been utilized to detect and classify various types of cancer, inc...

Single-cell RNA sequencing reveals immunological link between house dust mite allergy and childhood asthma.

Scientific reports
Allergic asthma in children is typically associated with house dust mites (HDM) as the key allergen. Nevertheless, the diagnostic rate remains below 60% due to the absence of specific symptoms and diagnostic markers, which hinders the implementation ...

Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning.

Scientific reports
BACKGROUND: Metabolic associated steatohepatitis (MASH) represents a severe subtype of metabolic associated fatty liver disease (MASLD), with an increased risk of progression to cirrhosis and hepatocellular carcinoma. The nomenclature shift from nona...

Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma.

BMC medical genomics
BACKGROUND: One of the most common and prevalent cancers is laryngeal squamous cell carcinoma (LSCC), which poses a great threat to the life and health of the patient. Nonetheless, it has been demonstrated that ubiquitination is crucial for the devel...

The interictal transcriptomic map of migraine without aura.

The journal of headache and pain
BACKGROUND: The present study aimed to deliver a replicable transcriptomic map of migraine without aura (MO) and its comprehensive, genome- and drug discovery focused analysis to identify hypotheses for future research- and clinical attempts.

Unsupervised machine learning-based stratification and immune deconvolution of liver hepatocellular carcinoma.

BMC cancer
BACKGROUND: Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer and a leading cause of cancer-related deaths globally. The tumour microenvironment (TME) influences treatment response and prognosis, yet its heterogeneity remains ...

Identification of genetic indicators linked to immunological infiltration in idiopathic pulmonary fibrosis.

Medicine
This study employed bioinformatics to investigate potential molecular markers associated with idiopathic pulmonary fibrosis (IPF) and examined their correlation with immune-infiltrating cells. Microarray data for IPF were retrieved from the Gene Expr...

Development of a prognostic model for osteosarcoma based on macrophage polarization-related genes using machine learning: implications for personalized therapy.

Clinical and experimental medicine
While neoadjuvant chemotherapy combined with surgical resection has improved the prognosis for patients with osteosarcoma, its impact on metastatic and recurrent cases remains limited. Immunotherapy is emerging as a promising alternative. However, th...

Cross study transcriptomic investigation of Alzheimer's brain tissue discoveries and limitations.

Scientific reports
Developing effective treatments for Alzheimer's disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptomic datasets and developing innovative computational analyses may yield novel molecular targets with ...