Hamid Reza Rabiee

DeepGenePrior uses deep learning to prioritize genes affected by copy number variants in neurodevelopmental disorders, identifying common genes across autism, schizophrenia, and developmental delay.

This paper introduces scGAN, a method to reduce batch effects in scRNA-seq data by using a generative adversarial network, and shows it improves clustering and gene identification in psychiatric disorders.

MaxHiC improves Hi-C and capture Hi-C analysis by robustly correcting biases and identifying significant chromatin interactions with better biological relevance.

CANCERSIGN identifies and classifies mutational signatures in cancer genomes using raw mutation data, enabling clustering and analysis of ICGC datasets to discover novel signatures.

This study develops a CNN-RNN deep learning method to automatically detect irregular rhythms and abnormal waveforms in field potentials from human iPSC-derived cardiomyocytes.