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Single-Cell Analysis

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Predicting cell properties with AI from 3D imaging flow cytometer data.

Scientific reports
Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA se...

Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.

Renal failure
BACKGROUND: Renal fibrosis is a critical factor in chronic kidney disease progression, with limited diagnostic and therapeutic options. Emerging evidence suggests RNA-binding proteins (RBPs) are pivotal in regulating cellular mechanisms underlying fi...

scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.

Communications biology
The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools o...

Integrating single-cell sequencing and machine learning to uncover the role of mitophagy in subtyping and prognosis of esophageal cancer.

Apoptosis : an international journal on programmed cell death
Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its poor prognosis. Mitophagy has a significant impact on the process of cancer progression. This study investigated the prognostic significa...

X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development.

Computers in biology and medicine
In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This...

Deciphering the role of cuproptosis in the development of intimal hyperplasia in rat carotid arteries using single cell analysis and machine learning techniques.

Scientific reports
UNLABELLED: This study aims to explore the regulatory role of cuproptosis in carotid intimal hyperplasia (IH), providing new insights into its pathophysiological mechanisms and potential diagnostic and therapeutic strategies.

Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.

Scientific reports
Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large om...

Integration of single-cell and bulk RNA sequencing data using machine learning identifies oxidative stress-related genes LUM and PCOLCE2 as potential biomarkers for heart failure.

International journal of biological macromolecules
Oxidative stress (OS) is a pivotal mechanism driving the progression of cardiovascular diseases, particularly heart failure (HF). However, the comprehensive characterisation of OS-related genes in HF remains largely unexplored. In the present study, ...

Single-Cell Array Enhanced Cell Damage Recognition Using Artificial Intelligence for Anticancer Drug Discovery.

Analytical chemistry
This work developed a cell damage recognition method based on single-cell arrays using an artificial intelligence tool. The method uses micropatterns (single-cell micropatches and microwells) to isolate each cell in an ordered array to minimize cell ...