Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences
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
- National University of Singapore,Jiangnan University
- Organisation type
- Academia,Academia
- Country
- Singapore, China
- Published
- 4 September 2024
- Authors
- Zhanglu Yan, Weiran Chu, Yuhua Sheng, Kaiwen Tang, Shida Wang, Yanfeng Liu, Weng-Fai Wong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Gene expression enhancement
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
- 133 tokens
Initial sequences: 73 × 15 = 1,095 codons New sequences: 6 × 10 × 15 = 900 codons Total: 1,095 + 900 = 1,995 codons Final estimate: 1.995e3
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
- Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences was published by National University of Singapore,Jiangnan University, in the country recorded as Singapore, during September 2024. The category the publisher falls under is academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of gene expression enhancement.
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 set ran to roughly 133 tokens of text.
Answers
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences — common questions
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences— what GPU do I need to run it?
None. This 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.
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences— who created it?
It was published by National University of Singapore,Jiangnan University, based in Singapore, an organisation categorised as academia,Academia.
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences— when was it released?
It was published in September 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.
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of gene expression enhancement. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.