Xinghu Qin, Associate Professor of Beijing Forestry University's Projects
3D de novo assembly (3D DNA) pipeline
Modifications to the adaptMT package including wrapper functions for XGBoost implementation with EM algorithm cross-validation tuning (see pre-print: https://www.biorxiv.org/content/early/2019/10/16/806471)
An R package for reproducible and automated ADMIXTOOLS analyses
Tools test whether admixture occurred and more
A beautiful, simple, clean, and responsive Jekyll theme for academics
ALFDA-Affinity based Local Multimodal Data Analysis
Anatomogram illustrating Expression Atlas experiments
Applied Computational Genomics Course at UU: Spring 2020
Sampling and manipulating genome-wide ancestral recombination graphs (ARGs)
The Hugo boilerplate we use for our projects.
vessel segmentation, artery and vein, optic disc, vascular feature analysis
A repository containing interesting demos / failure cases of ChatGPT
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tools for adding mutations to existing .bam files, used for testing mutation callers
bjfu.edu.qin_lab
Integrate multiple genome assemblies into a pangenome graph
This is a modified version of caret: (Classification And Regression Training). An R package that contains misc functions for training and plotting classification and regression models. Credits go to the caret package where the below link shows the usage and how to compile your own model.
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
R interface to Google Cloud Machine Learning Engine
π Circular and Rectangular Manhattan Plot
This is the official implementation of the method ContIG, for self-supervised learning from medical imaging with genomics
Jupyter notebook based genomic data visualization toolkit.
collection of notebooks with different cytoscape workflows
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
DA incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis). These discriminant analyses can be used to do ecological and evolutionary inference. We show the examples of demographic history inference, species identification, and population structure inference in the vignettes using the supervised discriminant analysis.