AIMC Topic: Transcriptome

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Defining lipedema's molecular hallmarks by multi-omics approach for disease prediction in women.

Metabolism: clinical and experimental
Lipedema is a chronic disease in females characterized by pathologic subcutaneous adipose tissue expansion and hitherto remains without druggable targets. In this observational study, we investigated the molecular hallmarks of lipedema using an unbia...

Screening necroptosis genes influencing osteoarthritis development based on machine learning.

Scientific reports
Machine learning can be applied to identify key genes associated with osteoarthritis (OA). This study aimed to explore the differential expression of necroptosis-related genes (NRGs) during the progression of OA, identify key gene modules strongly li...

Multi-omics integration and machine learning identify and validate neutrophil extracellular trap-associated gene signatures in chronic rhinosinusitis with nasal polyps.

Clinical immunology (Orlando, Fla.)
This study aimed to explore the molecular characteristics of neutrophil extracellular traps (NETs) in chronic rhinosinusitis with nasal polyps (CRSwNP). Differentially expressed gene analysis, weighted gene co-expression network analysis, and machine...

MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning.

Genome medicine
Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for un...

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 ...

Machine learning uncovers the transcriptional regulatory network for the production host Streptomyces albidoflavus.

Cell reports
Streptomyces albidoflavus is a widely used strain for natural product discovery and production through heterologous biosynthetic gene clusters (BGCs). However, the transcriptional regulatory network (TRN) and its impact on secondary metabolism remain...

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...

Machine learning analysis of gene expression profiles of pyroptosis-related differentially expressed genes in ischemic stroke revealed potential targets for drug repurposing.

Scientific reports
The relationship between ischemic stroke (IS) and pyroptosis centers on the inflammatory response elicited by cerebral tissue damage during an ischemic stroke event. However, an in-depth mechanistic understanding of their connection remains limited. ...

Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.

eLife
The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence suggests that gametes alter oviductal gene expression. To delineate the adaptive interactions between the oviduct and gamete/embryo, we performed a m...

Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.

PeerJ
BACKGROUND: Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosi...