AI Medical Compendium Topic

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Single-Cell Analysis

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Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network.

Briefings in bioinformatics
The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, w...

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Briefings in bioinformatics
Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various as...

FateNet: an integration of dynamical systems and deep learning for cell fate prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and ...

Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell.

Clinical and translational medicine
With rapid development and mature of single-cell measurements, single-cell biology and pathology become an emerging discipline to understand the disease. However, it is important to address concerns raised by clinicians as to how to apply single-cell...

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Journal of cellular and molecular medicine
Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating ...

scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Briefings in bioinformatics
Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive g...

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Journal of cellular and molecular medicine
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tu...

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Journal of cellular and molecular medicine
Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating ...

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

Journal of cellular and molecular medicine
The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cel...