AIMC Topic: Gene Expression Profiling

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Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls.

Biochimica et biophysica acta. Gene regulatory mechanisms
Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop ...

Integrating cellular experiments, single-cell sequencing, and machine learning to identify endoplasmic reticulum stress biomarkers in idiopathic pulmonary fibrosis.

Annals of medicine
BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) presents a severe respiratory challenge with a poor prognosis due to the lack of reliable biomarkers. Recent evidence suggests that Endoplasmic Reticulum Stress (ERS) may be associated with IPF pathogen...

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.

Scientific reports
The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA...

Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms.

Scientific reports
Cognitive dysfunction caused by diabetes has become a serious global medical issue. Diabetic kidney disease (DKD) exacerbates cognitive dysfunction in patients, although the precise mechanism behind this remains unclear. Here, we conducted an investi...

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.

Frontiers in immunology
BACKGROUND: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene sign...

Employ machine learning to identify NAD+ metabolism-related diagnostic markers for ischemic stroke and develop a diagnostic model.

Experimental gerontology
Ischemic stroke (IS) is a severe condition regulated by complex molecular alterations. This study aimed to identify potential nicotinamide adenine dinucleotide (NAD+) metabolism-associated diagnostic markers of IS and explore their associations with ...

Identifying Potential Diagnostic and Therapeutic Targets for Infantile Hemangioma Using WGCNA and Machine Learning Algorithms.

Biochemical genetics
Infantile hemangioma (IH) is the most common benign vascular tumor during infancy and childhood and is characterized by abnormal vascular development. It is the most common vascular tumor and its related mechanisms and treatments remain a problem. IH...

Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning.

Scientific reports
Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenviro...