The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of dr…

A two-phase Codex skill that turns figures, results, and `.bib` files into a full venue-aware LaTeX paper.

📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程

📚 从零开始构建大模型

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

Scripts for data and figure generation in SAVER paper

leeml-notes已更名为leedl-tutorial，请访问：https://github.com/datawhalechina/leedl-tutorial

Reproduce the results presented in paper Scouter.

Gene perturbation prediction using LLM embeddings

GenePert: Leveraging GenePT Embeddings for Gene Perturbation Prediction

freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations

GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations

scDisInFact is a single-cell data integration and condition effect prediction framework

scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism

Interpret perturbation responses from scRNA-seq perturbation experiments

PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation