AIMC Topic: Databases, Genetic

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Atrial fibrillation risk model based on LASSO and SVM algorithms and immune infiltration of key mitochondrial energy metabolism genes.

Scientific reports
Atrial fibrillation (AF) is a predominant cardiac arrhythmia with unclear etiology. This study used bioinformatics and machine learning to explore the relationship between mitochondrial energy metabolism-related genes (MEMRGs) and immune infiltration...

Identification of crucial genes for polycystic ovary syndrome and atherosclerosis through comprehensive bioinformatics analysis and machine learning.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: To identify potential biomarkers in patients with polycystic ovary syndrome (PCOS) and atherosclerosis, and to explore the common pathologic mechanisms between these two diseases in response to the increased risk of cardiovascular diseases...

Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis.

Mammalian genome : official journal of the International Mammalian Genome Society
BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide, and aberrant phagocytosis may be involved in its development. Understanding this aspect may provide new avenues for prompt CAD diagnosis.

Mitochondrial autophagy-related gene signatures associated with myasthenia gravis diagnosis and immunity.

Autoimmunity
Myasthenia gravis (MG) is a common autoimmune disorder that causes skeletal muscle weakness. Most patients presented with skeletal muscle weakness and endurance decline. Mitophagy refers to removing and interpreting aging or damaged mitochondria in c...

A machine learning-based investigation of integrin expression patterns in cancer and metastasis.

Scientific reports
Integrins, a family of transmembrane receptor proteins, are well known to play important roles in cancer development and metastasis. However, a comprehensive understanding of these roles has not been achieved due to the complex relationships between ...

A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.

Scientific reports
Gene microarray technology provides an efficient way to diagnose cancer. However, microarray gene expression data face the challenges of high-dimension, small-sample, and multi-class imbalance. The coupling of these challenges leads to inaccurate res...

Analysis and validation of programmed cell death genes associated with spinal cord injury progression based on bioinformatics and machine learning.

International immunopharmacology
BACKGROUND: Spinal cord injury (SCI) is a severe condition affecting the central nervous system. It is marked by a high disability rate and potential for death. Research has demonstrated that programmed cell death (PCD) plays a significant role in th...

Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers.

Frontiers in endocrinology
BACKGROUND: Diabetic foot ulcers (DFUs) are a serious complication of diabetes mellitus that manifests as chronic, non-healing wounds that have a significant impact on patients quality of life. Identifying key molecular targets associated with DFUs c...

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.

PloS one
Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common different...

Decoding the cytokine code for heart failure based on bioinformatics, machine learning and Bayesian networks.

Biochimica et biophysica acta. Molecular basis of disease
BACKGROUND: Despite maximal pharmacological treatment guided by clinical guidelines, the prognosis of heart failure (HF) remains poor, posing a significant public health burden. This necessitates uncovering novel pathological and cardioprotective pat...