DCPR_V1.0

<b>DCPR</b>This is a repository to deposit the code and data for DCPR model. DCPR is a deep learning framework for circadian phase reconstruction.<b>Main f</b><b>iles</b><b>AD Datasets</b>: Three Alzheimer's Disease(AD) datasets from different brain regions: Entorhinal Cortex (EC), Hippocampus (HIP), and Frontal Cortex (FC).<b>Synthetic datasets</b>: Twelve synthetic datasets differ in varying experimental conditions (e.g., time span, sampling interval, and noise levels).<b>GEO datasets</b>: Six GEO datasets with diffrent species, tissues and sequencing platforms.<b>Transcriptomic datasets: </b>Three additional GEO datasets for model test: the transcriptomic profiles of Bmal1 knockout and Cry1/2 knockout mouse model, normal aging, human skeletal muscle.<b>DCPR_codes</b>: Source code for DCPR model.<b>Visualization</b>: Scripts for data visualization, including circadian curves (cosine model fitting), sample plots (predicted and real time), model performance (CDF curves, and accuracy plots).<b>Supplementary File 1</b>: Seed gene sets derived from mouse peritoneal macrophages/hypothalamus and Papio anubis ileum.<b>Supplementary File 2</b>: Prior knowledge obtained from the Circadian Gene Database (CGDB) and related analysis of core clock genes from mouse liver.<b>Supplementary File 3</b>: Comparative analysis of differential rhythmic patterns in core clock genes between normal and Alzheimer's disease conditions across three brain region datasets (Entorhinal Cortex (EC), Hippocampus (HIP), and Frontal Cortex (FC)).<b>Configuration</b>It is recommended to use the conda environment (python 3.8). See environment.yaml for details.<b>How to use</b>refer to<b> </b>https://github.com/NJAU-CDSIC/DCPR_V1.0<br>