Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-08-01 | 862 |
| 2026-08-06 | 859 |
| 2026-08-10 | 860 |
| 2026-08-11 | 861 |
| 2026-08-12 | 861 |
| 2026-08-13 | 861 |
| 2026-08-18 | 861 |
| 2026-08-24 | 862 |
| 2026-08-27 | 863 |
| 2026-09-04 | 862 |
| 2026-09-12 | 863 |
| 2026-09-25 | 865 |
| 2026-09-30 | 867 |
| 2026-10-02 | 868 |
| 2026-10-07 | 867 |
| 2026-10-09 | 868 |
| 2026-10-12 | 867 |
| 2026-10-20 | 867 |
| 2026-11-04 | 867 |
Today
— stars today
This week
— stars this week
This month
-1 stars this month
Momentum
0.0
growth rate 0.00%/day
[](https://github.com/sindresorhus/awesome)
[](https://github.com/OmicsML/awesome-deep-learning-single-cell-papers/pulls)
# awesome-deep-learning-single-cell-papers
This repository keeps track of the latest papers on single-cell analysis with deep learning methods. We categorize them based on individual tasks.
We will try to make this list updated. If you find any error or any missed paper, please don't hesitate to open an issue or pull request.
## Citation
Be free to refer to our comprehensive survey paper on Deep Learning in Single-cell Analysis:
```bibtex
@article{molho2024deep,
title={Deep learning in single-cell analysis},
author={Molho, Dylan and Ding, Jiayuan and Tang, Wenzhuo and Li, Zhaoheng and Wen, Hongzhi and Wang, Yixin and Venegas, Julian and Jin, Wei and Liu, Renming and Su, Runze and others},
journal={ACM Transactions on Intelligent Systems and Technology},
volume={15},
number={3},
pages={1--62},
year={2024},
publisher={ACM New York, NY}
}
```
## For the foundation model for single-cell, more papers are recorded [[HERE]](https://github.com/OmicsML/awesome-foundation-model-single-cell-papers).
- [Book](#book)
- [Single Cell Technology](#single-cell-techonoly)
- [Course](#course)
- [Survey](#survey)
- [Pretrained Model or LLM or Foundation Model](#pretrained-model-or-llm-or-foundation-model)
- [GAN or Diffusion Model](#gan-or-diffusion-model)
- [Multimodal Learning](#multimodal-learning)
- [Single Cell Data Simulation](#single-cell-data-simulation)
- [Interpretability](#interpretability)
- [Spatialtemporal Transcriptomic](#spatialtemporal-transcriptomic)
- [RNA Velocity](#rna-velocity)
- [Molecular Representation Learning](#molecular-representation-learning)
- [Single Cell Perturbation or Drug Response](#single-cell-perturbation-or-drug-response)
- [Cellular Dynamics](#cellular-dynamics)
- [Single Cell Application](#single-cell-application)
- [Tools For Single Cell or Spatial Data](#tools-for-single-cell-or-spatial-data)
- [Single Cell Atlas](#single-cell-atlas)
- [Single Cell Visualization](#single-cell-visualization)
- [Benchmarking](#benchmarking)
- [Metric Design](#metric-design)
- [Subcellular Analysis](#subcellular-analysis)
- [Dimensionality Reduction and Visualization](#dimensionality-reduction-and-visualization)
- [Representation Learning](#representation-learning)
- [Batch Effect Correction](#batch-effect-correction)
- [Tumor Microenvironment-TME](#tumor-microenvironment-tme)
- [Cell-Cell Communication Events](#cell-cell-communication-events)
- [Gene Regulatory Network](#gene-regulatory-network)
- [Imputation](#imputation)
- [Spatial Domain](#spatial-domain)
- [Reference Embedding or Transfer Learning](#reference-embedding-or-transfer-learning)
- [Cell Segmentation](#cell-segmentation)
- [Cell Type Deconvolution](#cell-type-deconvolution)
- [Cell Type Annotation](#cell-type-annotation)
- [Cell Clustering](#cell-clustering)
- [Disease Prediction](#disease-prediction)
- [Multimodal Integration](#multimodal-integration)
- [Multiomics Translation](#multiomics-translation)
## Book
1. [[Single Cell Best Practices]](https://www.sc-best-practices.org/preamble.html), Fabian Theis's Lab
1. [[Basics of Single-Cell Analysis with Bioconductor]](http://bioconductor.org/books/3.15/OSCA.basic/index.html), Bioconductor software based on R
## Single Cell Technology
### Single-Modality
### Multimodality
### Spatial Transcriptomic
1. [2022 Nature Methods] **Museum of spatial transcriptomics** [[paper]](https://www.nature.com/articles/s41592-022-01409-2)
## Course
1. [[CSCI 1850 Deep Learning in Genomics]](https://cs.brown.edu/courses/csci1850/index.html), Brown University
1. [[Machine Learning in Genomics: Dissecting Human Disease Circuitry]](http://stellar.mit.edu/S/course/6/fa21/6.047/materialsExcerpt of 73,602 characters
Read on GitHub
292
6
6
6
4
3
3
Hongzhi Wen
2
Caffery Yang · Texas A&M University
1
Jérémie Kalfon · BroadInstitute - PiPle - PasteurInstitute
1
Alibaba Group · United States
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:58c38dd0ebf95a76, llm:Repository name: 'awesome-deep-learning-single-cell-papers' — an 'awesome' curated list of deep learning papers for single-cell (biology) analysis (implied by name).
matched fp:58c38dd0ebf95a76, llm:Repository name: 'awesome-deep-learning-single-cell-papers' — an 'awesome' curated list of deep learning papers for single-cell (biology) analysis (implied by name).
matched fp:58c38dd0ebf95a76, llm:Repository name: 'awesome-deep-learning-single-cell-papers' — an 'awesome' curated list of deep learning papers for single-cell (biology) analysis (implied by name).