Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage
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
- Sun Yat-sen University,University of California Irvine,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences
- Organisation type
- Academia,Academia
- Country
- China, United States of America
- Published
- 12 July 2024
- Authors
- Zhiwei Huang, Songhao Luo, Zihao Wang, Zhenquan Zhang, Benyuan Jiang, Qing Nie, Jiajun Zhang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Transcriptomic 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.
- Parameters
- 2.2K
- Training data
- tokens
- Epochs
- 200
"Firstly, the input layer contains 5 neurons " "Therefore, we identified a single hidden layer of 128 neurons and an output layer of 12 neurons as the appropriate model architecture" Parameter: 5*128+128*12=2176
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 3.9 × 10¹⁰ FLOP
2*2176*3*15000*200=39168000000
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
- Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage
- Last updated
- 28 November 2025
What the numbers mean
About this model
Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage was published by Sun Yat-sen University,University of California Irvine,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, in China, in July 2024. The organisation is categorised as academia,Academia.
It works in Biology, and is recorded as doing transcriptomic prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training run consumed about 3.9 × 10¹⁰ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage — common questions
When was Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage released?
Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage was published in July 2024. 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 learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage used for?
Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage works in Biology, and is recorded as handling transcriptomic prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage?
Around 3.9 × 10¹⁰ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage?
None. Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage 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 learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage open source?
The licensing for Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage have?
Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage has 2.2K parameters. "Firstly, the input layer contains 5 neurons " "Therefore, we identified a single hidden layer of 128 neurons and an output layer of 12 neurons as the appropriate model architecture" Parameter: 5*128+128*12=2176. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage?
Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage was published by Sun Yat-sen University,University of California Irvine,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, based in China, categorised as academia,Academia.
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