AIMC Topic: Single-Cell Gene Expression Analysis

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scSemiPLC: a semi-supervised learning framework for annotating single-cell RNA-Seq data by generating pseudo-labels through clustering.

mSystems
UNLABELLED: Single-cell RNA sequencing (scRNA-seq) technology enables researchers to explore heterogeneity of diverse cell types within complex tissues at the single-cell resolution. Cell annotation, as a crucial step in scRNA-seq data analysis, prov...

Denoising single-cell RNA-seq data with a deep learning-embedded statistical framework.

BMC bioinformatics
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides extensive opportunities to explore cellular heterogeneity but is often limited by substantial technical noise and variability. The prevalence of zero counts, arising from both biological var...

Deciphering lactate/lactylation networks in AML: integrated scRNA-seq and transcriptomics reveal functions and prognostic model.

BMC cancer
Acute myeloid leukemia (AML) exhibits pronounced heterogeneity, necessitating deep molecular characterization for precision therapy. Lactate metabolism and histone lactylation, influencing tumor biology via epigenetic regulation and immune microenvir...

Single-cell RNA-seq combined with bulk RNA-seq analysis identifies necroptosis-related genes as therapeutic targets for periodontitis.

BMC medical genomics
BACKGROUND: Necroptosis, a regulated form of programmed cell death, exacerbates inflammatory responses by releasing damage-associated molecular patterns and inflammatory factors. However, the specific mechanisms underlying necroptosis in periodontiti...

Paired snRNA-seq and scRNA-seq analysis of MASLD patients to identify early-stage markers for disease progression.

Hepatology communications
BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease worldwide. Progression from simple metabolic dysfunction-associated steatotic liver (MASL) without necro-inflammation to...

Machine learning integration of bulk and single-cell RNA-seq data reveals glycolytic heterogeneity in colorectal cancer.

Medical oncology (Northwood, London, England)
As one of the most prevalent malignancies worldwide, colorectal cancer (CRC) exhibits a strong metabolic dependency on glycolysis, which fuels tumor expansion and shapes an immunosuppressive microenvironment. Despite its clinical significance, the re...

CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning.

BMC biology
BACKGROUND: The advent of single-cell RNA sequencing (scRNA-seq) has provided unprecedented insights into cancer cellular diversity, enabling a comprehensive understanding of cancer at the single-cell level. However, identifying cancer cells remains ...

Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer.

BMC bioinformatics
Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression by allowing researchers to analyze the transcriptomes of individual cells. This technology provides unprecedented insights into cellular heterogeneity, cellular st...

scGANSL: Graph Attention Network with Subspace Learning for scRNA-seq Data Clustering.

Journal of chemical information and modeling
Single-cell RNA sequencing (scRNA-seq) has become a crucial technology for analyzing cellular diversity at the single-cell level. Cell clustering is crucial in scRNA-seq data analysis as it accurately identifies distinct cell types and uncovers poten...