Alzheimer's Disease is progressing as the most common cause of neurological disorder worldwide. This tool aims to use Artifical Neural Network (Deep Learning) model to classify Normal Control(NC) patients and Alzheimer’s Disease(AD) patients from their single cell RNA seq data. The tool takes 10x single cell genomics data as input and predicts whether the patient is diseased or healthy with the help of highly trained model. An excellent feature selection method called mRMR (Minimum Redundancy Maximum Relevance) was used to find out top 100 features for classification. Followed by Incremental Feature Selection (IFS) which led to identification of a subset of 35 genes which act as promising biomarkers in classification and prediction of Normal and Diseased patients.
Reference: Srivastava et al. Prediction of Alzheimer’s Disease from Single Cell Transcriptomics Using Deep Learning. bioRxiv; 2023. DOI: 10.1101/2023.07.07.548171.