AIMC Topic: Gene Expression Profiling
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Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis.
Biochemical genetics
Apr 24, 2024
Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms an...
Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.
ESC heart failure
Apr 24, 2024
AIMS: Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant thera...
Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques.
Frontiers in cellular and infection microbiology
Apr 23, 2024
OBJECTIVE: The relationship between macrophages and the gut microbiota in patients with atherosclerosis remains poorly defined, and effective biological markers are lacking. This study aims to elucidate the interplay between gut microbial communities...
Receptors, CCR1
CD68 Molecule
Agammaglobulinaemia Tyrosine Kinase
Single-Cell Analysis
Antigens, Differentiation, Myelomonocytic
Gene Regulatory Networks
Macrophages
Interferon Regulatory Factors
Gastrointestinal Microbiome
Biomarkers
Humans
Antigens, CD
Gene Expression Profiling
Machine Learning
Atherosclerosis
Algorithms
Plaque, Atherosclerotic
Explicate molecular landscape of combined pulmonary fibrosis and emphysema through explainable artificial intelligence: a comprehensive analysis of ILD and COPD interactions using RNA from whole lung homogenates.
Medical & biological engineering & computing
Apr 22, 2024
Combined pulmonary fibrosis and emphysema (CPFE) presents a unique challenge in respiratory disorders, merging features of interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD). Using the random forest algorithm, our study ...
Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.
Aging
Apr 22, 2024
BACKGROUND: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms.
Unveiling the link between lactate metabolism and rheumatoid arthritis through integration of bioinformatics and machine learning.
Scientific reports
Apr 22, 2024
Rheumatoid arthritis (RA) is a persistent autoimmune condition characterized by synovitis and joint damage. Recent findings suggest a potential link to abnormal lactate metabolism. This study aims to identify lactate metabolism-related genes (LMRGs) ...
Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.
Frontiers in immunology
Apr 22, 2024
BACKGROUND: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity...
Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.
ESC heart failure
Apr 17, 2024
AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering th...
Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression.
Artificial intelligence in medicine
Apr 17, 2024
For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of G...
CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.
PLoS computational biology
Apr 17, 2024
Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference mod...
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