AIMC Topic: Gene Expression Profiling

Clear Filters Showing 1351 to 1360 of 1601 articles

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.

NPJ systems biology and applications
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a...

Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer.

Journal of translational medicine
BACKGROUND: Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make ...

Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts.

Lipids in health and disease
BACKGROUND: Dysregulated programmed cell death pathways mechanistically contribute to hepatic inflammation and fibrogenesis in non-alcoholic steatohepatitis (NASH). Identification of cell death genes may offer insights into diagnostic and therapeutic...

Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning.

BMC neurology
BACKGROUND: COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for both venous and arterial thrombosis. This heightened risk contributes to an increased probability o...

Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression Network Analysis and Machine Learning.

International journal of molecular sciences
Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying key genes and pathways involved in sepsis progression is crucial for improving diagnostic and therape...

Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.

PLoS biology
The study of ubiquitous circadian rhythms in human physiology requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a ...

Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning.

The journal of gene medicine
Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant global health challenges. COPD is a common comorbidity in nonsmall cell lung cancer (NSCLC) patient...

Exploration of immune-related diagnostic biomarkers in unexplained infertility by bioinformatics analysis and machine learning.

Taiwanese journal of obstetrics & gynecology
OBJECTIVE: We aimed to discover the biomarkers associated with UI and their correlation with immune cell infiltration.

Identification of Novel Diagnostic Markers for Atherosclerosis Using Machine-Learning Algorithms.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP
OBJECTIVE: To outline immune-cell infiltration and identify diagnostic genes for atherosclerosis (AS) to better understand the potential molecular processes involved in AS development.