AIMC Topic: Single-Cell Analysis

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CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer.

Genome biology
Machine learning methods, especially Transformer architectures, have been widely employed in single-cell omics studies. However, interpretability and accurate representation of out-of-distribution (OOD) cells remains challenging. Inspired by the glob...

WISP2/CCN5 revealed as a potential diagnostic biomarker for endometriosis based on machine learning and single-cell transcriptomic analysis.

Functional & integrative genomics
OBJECTIVE: Endometriosis is a prevalent gynecological disease characterized by the ectopic growth of functional endometrial tissue outside the uterine cavity, affecting millions of women worldwide. Currently, the definitive diagnosis relies on invasi...

Machine learning algorithms integrate bulk and single-cell RNA data to reveal the crosstalk and heterogeneity of NOTCH and autophagy activity following idiopathic pulmonary fibrosis.

International immunopharmacology
BACKGROUND: NOTCH and autophagy significantly impact the pathogenesis of idiopathic pulmonary fibrosis (IPF); however, studies exploring their heterogeneity and potential correlation at the single-cell level are still lacking. Identifying the feature...

Single-cell sequencing and machine learning reveal the role of dioxin-interacting genes in HCC prognosis and immune microenvironment.

Ecotoxicology and environmental safety
Dioxins are persistent environmental pollutants that bioaccumulate in the food chain, posing significant risks to human health. Despite their low environmental concentrations, dioxins accumulate in tissues, particularly in top predators and humans, r...

Neurodegeneration Promotes Tumorigenesis in Colorectal Cancer: Insights From Single-Cell and Spatial Multiomics.

JCO precision oncology
PURPOSE: Colorectal cancer (CRC) ranks third in global incidence and second in mortality, with rates increasing among younger populations. The enteric nervous system (ENS) is crucial for gastrointestinal function, and its dysfunction is associated wi...

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...

SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution.

BMC bioinformatics
BACKGROUND: Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates.

TRAF3 as a potential diagnostic biomarker for recurrent pregnancy loss: insights from single-cell transcriptomics and machine learning.

BMC pregnancy and childbirth
BACKGROUND: Recurrent pregnancy loss (RPL), characterized by multiple miscarriages, remains a condition with unclear etiology, posing significant challenges for affected women and couples. This study aims to explore the underlying mechanisms of RPL, ...

Disulfide bond-related gene signature development for bladder cancer prognosis prediction and immune microenvironment characterization.

Scientific reports
Bladder cancer is the fourth most common malignant tumor in men, with limited therapeutic biomarkers and heterogeneous responses to immunotherapy. Disulfide bond-driven cell death has emerged as a critical regulator of tumor progression and immune mi...

Single-cell data combined with phenotypes improves variant interpretation.

BMC genomics
BACKGROUND: Whole genome sequencing offers significant potential to improve the diagnosis and treatment of rare diseases by enabling the identification of thousands of rare, potentially pathogenic variants. Existing variant prioritisation tools can b...