A Deep Learning-Based Model for Quantifying Transposable Elements in Single-Cell Sequencing Data

Single-cell multi-omics integration using Optimal Transport

Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

Computational pipeline to process EasySci-RNA data.

Single-cell Proliferation Rate Inference in Non-homogeneous Tumours through Evolutionary Routes (SPRINTER)

Hotspot cluster distance analysis in spatial transcriptomic data

This is an Diaz Lab internal mouse WES somatic variant calling pipeline (primary analysis only) using GATK/Mutect

Detecting and dissecting anomalous anatomic regions in spatial transcriptomics with STANDS

This is the Study Guide for Learn Machine Learning in 3 Months (PyTorch Curriculum) by Siraj Raval on Youtube

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。

HMM-integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data

Graspot: A graph attention network for spatial transcriptomics data integration with optimal transport

IOBR is an R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology.

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

tools and notes for spatial omics

STREAMLINE (Topological benchmarking of GRN inference from scRNA-Sequencing data)

CAST package

R package to visualize gene expression data based on weighted kernel density estimation

Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

Heterogeneous graph attention network improves cancer multiomics integration

A knowledge graph and a set of tools for drug repurposing

An PyTorch implementation of graph neural networks (GCN, GraphSAGE and GAT) that can be simply imported and used.

InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop