The circular-Sliding Window Association Test (c-SWAT) is a novel deep learning approach that considers correlations between features, utilizing modules such as WGCNA (Weighted Gene Co-expression Network Analysis) to enhance classification prediction performance.

• Python 3.6.10
• For other dependencies, check requirements.txt.

python c-SWAT.py data/ADNI_norm.csv data/wgcna.txt data/classes.txt 

For questions or support, please contact [tjo(at)iu.edu].

🔖 c-SWAT citation:

Jo, Taeho, et al. "Circular-SWAT for deep learning based diagnostic classification of Alzheimer’s disease: Application to metabolome data." eBioMedicine (accepted, 2023)

🔖 Example of SWAT application:

Jo, Taeho, et al. "Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification." Briefings in Bioinformatics (2022)