AIMC Topic: Gene Expression Profiling
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Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.
Scientific reports
May 19, 2025
Osteosarcoma, an aggressive bone malignancy predominantly affecting children and adolescents, is characterized by a poor prognosis and high mortality rates. The development of reliable prognostic tools is critical for advancing personalized treatment...
Similarity of immune-associated markers in COVID-19 and Kawasaki disease: analyses from bioinformatics and machine learning.
BMC pediatrics
May 19, 2025
BACKGROUND: Infection by the SARS-CoV-2 virus can cause coronavirus disease 2019 (COVID-19) and can also exacerbate the symptoms of Kawasaki disease (KD), an acute vasculitis that mostly affects children. This study used bioinformatics and machine le...
Identification of hub biomarkers in coronary artery disease patients using machine learning and bioinformatic analyses.
Scientific reports
May 18, 2025
Understanding the molecular underpinnings of CAD is essential for developing effective therapeutic strategies. This study aims to identify and analyze differentially expressed hub biomarkers in the peripheral blood of CAD patients. Based on RNA-seq d...
Integrating bioinformatics and machine learning to unravel shared mechanisms and biomarkers in chronic obstructive pulmonary disease and type 2 diabetes.
Postgraduate medical journal
May 18, 2025
BACKGROUND: Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are on the rise. While there is evidence of a link between the two diseases, the pathophysiological mechanisms they share are not fully understood.
MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity.
International journal of molecular sciences
May 15, 2025
Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale multi-omic data...
Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.
Scientific reports
May 15, 2025
Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as a critical microvascular complication associated with high mortality rate...
Integrating Bulk and Single-Cell Transcriptomics with Machine Learning Reveals a Heme Metabolism-Based Panel for Lung Adenocarcinoma Chemotherapy Resistance.
International journal of molecular sciences
May 14, 2025
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality, with heme metabolism playing a critical role in tumor progression and treatment resistance. This study investigates the clinical implications of heme metabolism in LUAD, focus...
Drug Resistance, Neoplasm
Adenocarcinoma of Lung
Multidrug Resistance-Associated Proteins
Multidrug Resistance-Associated Protein 2
Gene Expression Regulation, Neoplastic
Ferroptosis
Cisplatin
Heme
Cell Line, Tumor
Single-Cell Analysis
Lung Neoplasms
Humans
Female
Machine Learning
Antineoplastic Agents
Prognosis
Gene Expression Profiling
Transcriptome
scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.
Cell genomics
May 14, 2025
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
May 14, 2025
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
May 14, 2025
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 ...
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