SynOmicsBench
SynOmicsBench is the first benchmarking study tailored to high-dimensional clinical transcriptomic cancer data, comparing synthetic data generation methods across three clinical cancer trials.
Project
MIL 3D-GNN for Cdr1 Inhibitor Screening
This Master’s project explores the application of Multi-Instance Learning (MIL) 3D Graph Neural Networks for discovering novel inhibitors of the Candida albicans Cdr1 efflux pump.
Synergy of Advanced Machine Learning and Deep Neural Networks with Consensus Molecular Docking for Enhanced Potency Prediction of ALK Inhibitors
This is a repository containing my graduation thesis, which has been published in the Journal of Computer-Aided Molecular Design.
A Graph Neural Network Model Enables Accurate Prediction of Anaplastic Lymphoma Kinase Inhibitors Compared to Other Machine Learning Models
This repository contains my publications from the KSE international conferences (Proceedings of the IEEE) and also the most interesting parts of my graduation thesis.
In silico modeling for prediction of potential HIV integrase inhibitors
This is a repository containing Tieu Long Phan master thesis