AIMC Topic: Gene Expression Profiling

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The Construction of a New Prognostic Model of Breast Cancer and the Exploration of Drug Sensitivity Based on Machine Learning for Glycosylation-Related Genes.

Clinical breast cancer
AIMS: Breast cancer has become the number 1 killer threatening women's health. In recent years, glycosylation modification has played an increasingly important role in tumor progression. The aim of this study was to explore the key genes that may be ...

Mechanistic exploration of hexokinase 2 and metabolism in diabetic cardiomyopathy.

Molecular medicine reports
The pathogenesis of diabetic cardiomyopathy (DCM) remains incompletely understood. The present study employed weighted gene co‑expression network analysis to analyze the DCM transcriptome dataset from the Gene Expression Omnibus (GEO) database to ide...

Screening and preliminary analysis of antimicrobial peptide genes in Octopussinensis.

Fish & shellfish immunology
Antimicrobial peptides (AMPs) are small molecular peptides that widely exist in organisms to resist external microbial invasion and play a crucial role in the host's immune defense system. Owing to their functions of efficient broad-spectrum killing ...

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.

Breast (Edinburgh, Scotland)
PURPOSE: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused ...

Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI.

Computational biology and chemistry
Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single point in time. In other words, transcriptomics as it is currently practiced is two-dimensional (2DT...

Multi-omics identifies OSM-OSMR as a key receptor-ligand in the tumor environment of endometrial adenocarcinoma.

International immunopharmacology
Endometrial adenocarcinoma carries a bleak prognosis, and the molecular markers that evaluate the progression of endometrial adenocarcinoma to advanced stages remain uncertain. Cell-cell communication plays a crucial role in the tumor microenvironmen...

Pan-Cancer Spatial Profiling Reveals Conserved Subtypes and Niches of Cancer-Associated Fibroblasts.

Cancer research
Solid cancers are complex "ecosystems" comprised of diverse cell types and extracellular molecules, in which heterotypic interactions significantly influence disease etiology and therapeutic response. However, our current understanding of tumor micro...

Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.

Briefings in bioinformatics
Single-cell sequencing technology has profoundly revolutionized the field of cancer genomics, enabling researchers to explore gene expression profiles at the resolution of individual cells. Despite its extensive applications in the study of cancer ge...

Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.

Cancer research
UNLABELLED: Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving ch...

Global Thyroid Cancer Patterns and Predictive Analytics: Integrating Machine Learning for Advanced Diagnostic Modelling.

Journal of cellular and molecular medicine
BACKGROUND: The global increase in thyroid cancer prevalence, particularly among female populations, underscores critical gaps in our understanding of molecular pathogenesis and diagnostic capabilities. Our investigation addresses these knowledge def...