Deep-LDA
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Fudan University
- Organisation type
- Academia
- Country
- China
- Published
- 14 September 2022
- Authors
- Yunhe Liu, Qiqing Fu, Chenyu Dong, Xiaoqiong Xia, Gang Liu, Lei Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Cell Biology, Gene expression profile generation, Cell-cell interaction prediction
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
after removal of inferred impurity cells (Phase III; 10374 cells left)
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
The three deep generative models covered in this article, as well as necessary clustering evaluation tools and plotting tools are organized into the ScDeepTools (https://github.com/liuyunho/ScDeepTools) software. License: BSD-3-Clause license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Optimization and redevelopment of single-cell data analysis workflow based on deep generative models
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Deep-LDA was published by Fudan University, in China, in September 2022. The organisation is categorised as academia.
It works in Biology, and is recorded as doing cell Biology, Gene expression profile generation, Cell-cell interaction prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Deep-LDA — common questions
How many parameters does Deep-LDA have?
No parameter count has been published for Deep-LDA, which is why no memory or speed figure appears on this page.
Who created Deep-LDA?
Deep-LDA was published by Fudan University, based in China, categorised as academia.
When was Deep-LDA released?
Deep-LDA was published in September 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Deep-LDA used for?
Deep-LDA works in Biology, and is recorded as handling cell Biology, Gene expression profile generation, Cell-cell interaction prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Deep-LDA?
None. Deep-LDA is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Deep-LDA open source?
No. Deep-LDA has not had its weights published, so it exists only as a service controlled by its owner.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.