AIMC Topic: Databases, Genetic

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Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.

Frontiers in endocrinology
PURPOSE: We aimed to identify the mitochondria-related feature genes associated with type 2 diabetes mellitus and explore their potential roles in immune cell infiltration.

A novel seven-tier framework for the classification of MEFV missense variants using adaptive and rigid classifiers.

Scientific reports
There is a great discrepancy between the clinical categorization of MEFV gene variants and in silico tool predictions. In this study, we developed a seven-tier classification system for MEFV missense variants of unknown significance and recommended a...

ASVirus: A Comprehensive Knowledgebase for the Viral Alternative Splicing.

Journal of chemical information and modeling
Viruses are significant human pathogens responsible for pandemic outbreaks and seasonal epidemics. Viral infectious diseases impose a devastating global burden and have a profound impact on public health systems. During viral infections, alternative ...

Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.

Scientific reports
Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major contributor to cardiovascular diseases. KCTD10, a protein implicated in a variety of biological pro...

Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.

Frontiers in immunology
INTRODUCTION: Sepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ dysfunction. Despite medical advancements, the critical need for research into dependable diagnostic markers ...

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.

Biomolecules & biomedicine
Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in systemic lu...

Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach.

Scientific reports
The discovery of unique microRNA (miR) patterns and their corresponding genes in sarcoma patients indicates their involvement in cancer development and suggests their potential use in medical management. MiRs were identified from The Cancer Genome At...

ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies.

Journal of biomedical informatics
Gene selection is crucial for cancer classification using microarray data. In the interests of improving cancer classification accuracy, in this paper, we developed a new wrapper method called ieGENES for gene selection. First we proposed a parsimoni...

Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study.

Frontiers in immunology
INTRODUCTION: Knee osteoarthritis (KOA) is a degenerative joint disease characterized by the progressive deterioration of cartilage and synovial inflammation. A critical mechanism in the pathogenesis of KOA is impaired efferocytosis in synovial tissu...

Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis.

Frontiers in immunology
BACKGROUND: Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a criti...