Name: Obada Al Zoubi
Type: User
Company: Harvard Medical School/Broad Institute
Bio: I'm a biomedical and AI research scientist working on AI/ML for Life Sciences . Interested in drug discovery, molecular modeling, gene-therapy and more.
Twitter: Obada_alzoubi
Location: USA
Blog: www.obadaalzoubi.com
Obada Al Zoubi's Projects
Ready to use implementations of various Deep Learning algorithms using TensorFlow.
A collection of deep learning works on genomics and healthcare. The repo can help developers and researchers to have access to datasets and codes across the globe.
A neural network framework for predicting the Hi-C chromatin interactions from megabase scale DNA sequence
A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)
Implementation of a deep convolutional neural network for predicting chromatin features from DNA sequence
A GAN-based method for Enhancing Hi-C data
A list of deep learning implementations in biology
This repository contains the implementation of deep learning courses by Andrew ng on Coursera
Supporting evidence of EEG-ms mapping manuscript
EEG Features to be extract from raw data.
A list of openly available datasets in (mostly human) electrophysiology.
A step by step guide on how to use eMAGMA, an approach to conducting eQTL informed gene-based tests.
Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks
A R package designed to help people with clumsy fingers.
Analysis of the genomic architecture and functional role of myocardial trabeculae
R-package for structural equation modeling based on GWAS summary data
This demo for github actions and demo
Chromatin interaction aware gene regulatory modeling with graph attention networks
Neural indicators of gut-brain mechanosensation in humans manuscript codes
Codes and Scripts used in manuscript: "Hierarchical Fusion Evolving Spiking Neural Network for Adaptive Learning"
Workshop on measuring, analyzing, and visualizing the 3D genome with Hi-C data.
HiC-Reg is a tool to predict Hi-C contact counts from one-dimensional regulatory signals