Pinned Repositories
captum
Model interpretability and understanding for PyTorch
chip-seq-pipeline2
ENCODE ChIP-seq pipeline
DeepExplain
A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability
DNABERT
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
DNABERT_2
The official implementation of DNABERT-2.
espresso
EUGENe
Elucidating the Utility of Genomic Elements with Neural Nets
interpretation-deeplift
Public facing deeplift repo
logomaker
Software for the visualization of sequence-function relationships
Lovetree-
Luoyunfan-Jason's Repositories
Luoyunfan-Jason/optuna
A hyperparameter optimization framework
Luoyunfan-Jason/sam
Code for paper Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing.
Luoyunfan-Jason/captum
Model interpretability and understanding for PyTorch
Luoyunfan-Jason/DNABERT_2
The official implementation of DNABERT-2.
Luoyunfan-Jason/EUGENe
Elucidating the Utility of Genomic Elements with Neural Nets
Luoyunfan-Jason/SeqExplainer
Interpreting sequence-to-function machine learning models
Luoyunfan-Jason/DNABERT
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Luoyunfan-Jason/espresso
Luoyunfan-Jason/Lovetree-
Luoyunfan-Jason/REPAC
Luoyunfan-Jason/RNA-splicing---Pangolin
Pangolin is a deep-learning method for predicting splice site strengths.
Luoyunfan-Jason/RNA-splicing---SpliceAI
A deep learning-based tool to identify splice variants
Luoyunfan-Jason/logomaker
Software for the visualization of sequence-function relationships
Luoyunfan-Jason/ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
Luoyunfan-Jason/chip-seq-pipeline2
ENCODE ChIP-seq pipeline
Luoyunfan-Jason/interpretation-deeplift
Public facing deeplift repo
Luoyunfan-Jason/DeepExplain
A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability