AIMC Topic: Databases, Genetic

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Identification of key genes and development of an identifying machine learning model for sepsis.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
OBJECTIVE AND DESIGN: This study aims to identify key genes of sepsis and construct a model for sepsis identification through integrated multi-organ single-cell RNA sequencing (scRNA-seq) and machine learning.

Transcriptomic bioinformatics analysis proposes a novel BCL2-MAPK14-TXN oxidative stress diagnostic model of sepsis and identifies TXN as an oxidative stress-related signature gene in sepsis.

International immunopharmacology
BACKGROUND: Oxidative stress was one of key factors driving the septic development by the uncontrolled accumulation of free radicals, thus oxidative stress-related biomarkers provide a novel diagnostic option. This study focused on screening oxidativ...

Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke.

Scientific reports
Ischemic stroke (IS) is a multifactorial disease caused by the interaction of a variety of environmental and genetic factors, which can lead to severe disability and heavy social burden. This study aimed to find potential biomarkers related to T cell...

Immunogenic cell death biomarkers for sepsis diagnosis and mechanism via integrated bioinformatics.

Scientific reports
Immunogenic cell death (ICD) has been implicated in sepsis, a condition with high mortality, through mechanisms involving endoplasmic reticulum stress and other pathophysiological pathways. This study aimed to identify and validate ICD-related biomar...

Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus erythematosus.

Scientific reports
Evidence indicates a connection between periodontitis (PD) and systemic lupus erythematosus (SLE), though the underlying co-morbid mechanisms remain unclear. This study sought to identify the genetic factors and potential therapeutic agents involved ...

Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.

Scientific reports
Anoikis and immune cell infiltration are pivotal factors in the pathophysiological mechanism of diabetic nephropathy (DN), yet a comprehensive understanding of the mechanism is lacking. This work aimed to pinpoint distinctive anoikis-related genes (A...

Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis.

Scientific reports
Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however, its molecular mechanism remains unclear. Herein, hub genes and regulatory mechanisms in sepsis was investigated. The GSE65682 and GSE95233 datasets ...

Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research.

PloS one
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. T...

Integrating WGCNA and SVM-RFE identifies hub molecular biomarkers driving ischemic stroke progression.

Neurological research
BACKGROUND: Stroke is the second most common cause of death worldwide and the leading cause of long-term severe disability with neurological impairment worsening within hours after stroke onset and being especially involved with motor function. So fa...