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Scripts for virtual screening, cross docking and protein relax using Schrödinegr and Rosetta
Supporting scripts used for data analysis of journal publication DOI:10.1101/2024.03.28.587009
RNA-seq pipeline for raw sequence alignment and transcript/gene quantification.
🚀🚀 「大模型」2小时完全从0训练26M的小参数GPT！🌏 Train a 26M-parameter GPT from scratch in just 2h!
A PyTorch implementation of "Automatic Identification and Virtual Directed Evolution of Antimicrobial Peptides with Explainable Deep Learning".
A repository for setting up a RNAseq workflow
A computational method to rank and infer drug-responsive cell population towards in-silico drug perturbation using a target-perturbed gene regulatory network (tpGRN) for single-cell transcriptomic …
tumour immune status prediction with deep learning
Machine learning-based integration model with elegant performance
Final version of the code used to pre-process the raw data and create figures and tables for the manuscript titled "Stem-like T cells are associated with the pathogenesis of ulcerative colitis in h…
This repo includes all codes and intermediate for the manuscript of the TimiGP-Response. Of note, it includes the cell-cell interaction network of each dataset for the pan-cancer immune landscape a…
Single-cell RNA-seq analysis scripts and Jupyter notebooks for Quinn et al 2024, Nature Communications publication. Collaboration between Sequeira Lab and Byrd Lab.
Anti-Cancer Peptide Prediction with Deep Representation Learning Features