Head and neck cancer, which encompasses a range of malignancies affecting the respiratory tract and upper digestive tract and also the seventh most common cancer in the world. This tool aims to use Artifical Neural Network (Deep Learning) model to classify Normal Control (NC) patients and Head and Neck Cancer (HNSCC) 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 which act as promising biomarkers in classification and prediction of Normal and Diseased patients. Also further classified diseased patients into HPV+ and HPV-.
Reference: Jarwal A., Dhall A., Arora A., Patiyal S., Srivastava A. and Raghava GPS (2024) A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics. Frontiers in Molecular Biosciences DOI=10.3389/fmolb.2024.1395721