AIMC Topic: Single-Cell Analysis

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Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.

Communications biology
Single-cell image analysis is crucial for studying drug effects on cellular morphology and phenotypic changes. Most studies focus on single cell types, overlooking the complexity of cellular interactions. Here, we establish an analysis pipeline to ex...

A novel coarsened graph learning method for scalable single-cell data analysis.

Computers in biology and medicine
The emergence of single-cell technologies, including flow and mass cytometry, as well as single-cell RNA sequencing, has revolutionized the study of cellular heterogeneity, generating vast datasets rich in biological insights. Despite the effectivene...

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

Cell genomics
DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low covera...

Biases in machine-learning models of human single-cell data.

Nature cell biology
Recent machine-learning (ML)-based advances in single-cell data science have enabled the stratification of human tissue donors at single-cell resolution, promising to provide valuable diagnostic and prognostic insights. However, such insights are sus...

Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma.

International journal of medical sciences
Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. In this study, an integrated framework of...

Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.

PloS one
BACKGROUND: Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. The...

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...

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...

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...