OverviewThis repository contains the code and processed datasets for the manuscript: “Graph convolutional network model of CD4+ T cells provides an optimal single-cell clock for human age prediction”.This study demonstrates that utilizing single-cell Graph Convolutional Networks (scGCN) with gene-gene interaction graphs from CD4+ T cells achieves optimal age prediction performance compared to traditional machine learning models. Project WorkflowPlease run the scripts in the following folders sequentially: 01_seurat_analysis: Single-cell data preprocessing using Seurat (quality control, normalization, and cell-type extraction). 02_graph_construction: Building gene-gene interaction graphs based on the STRING database. 03_age_prediction_analysis: Training and comparing five distinct age prediction models across different cell types.
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".