Xinghu Qin, Associate Professor of Beijing Forestry University's Projects
scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation.
Interpretation by Deep Generative Masking for Biological Sequences
R scripts for Jumentier et al. 2021
A list of scRNA-seq analysis tools
Pipeline to take VCF through to Selection Analysis.
a framework for training sequence-level deep learning networks
A synthetic tool to study species genetic correlations (SGDCs), including α-SGDCs and β-SGDCs. The package includes many functions that can study the effects of enviromnetal factors on SGDCs.
A game theoretic approach to explain the output of any machine learning model.
Shapley Values with H2O AutoML Example (ML Interpretability)
Create genomic databases with SIFT predictions. Input is an organism's genomic DNA (.fa) file and the gene annotation file (.gtf). Output will be a database that can be used with SIFT4G_Annotator.jar to annotate VCF files.
Course material in notebook format for learning about single cell bioinformatics methods
Website hosted at https://sib-swiss.github.io/single-cell-training/latest/
Graph Neural Networks with Keras and Tensorflow 2.
SS_performance
Stabilized Hierarchical DNN with multiple knockoffs
Inferring feature importance in high-dimensional data
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Overview of the data QC, code, and GWAS summary output from the 2017 UK Biobank data release
genetic correlation between phenotypes in the UK biobank
VariantScan: a Machine Leaning Tool for Variant Association Testing
XGBoost Feature Interactions & Importance
The next-gen reverse proxy for full site mirroring