AlzGenPred is a CatBoost based method developed by using network-based features. It can classify the Alzheimer's Disease (AD) associated genes with 96.55% accuracy and 98.99% AUROC. AlzGenPred is developed as a standalone software package for the classification of AD genes. The method is tested on the AlzGene dataset and it outperformed with 96.43% accuracy. The accuracy on the experimental data represents that the AlzGenPred can correctly classify the AD-associated genes. Additionaly, the AlzGenPred is also validated on the transcriptomics dataset.

AlzGenPred is developed to provide the facility to the researcher to classify the AD genes out of thousands of the genes generated by multi-omics technologies. The small pool of genes out of thousands of genes predicted by AlzGenPred can be further used for research purpose. A copy of this tool is also submitted to GitHub.

Workflow of AlzGenPred