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GSR: Simulator - MichiGAN
| Attribute | Value |
|---|---|
| Title | MichiGAN |
| Short Description | MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks |
| Long Description | MichiGAN is a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment. |
| Keywords | Cellular identity, Disentangled representations, Generative adversarial networks, Representation learning, Single-cell genomics |
| Version | 1.0 |
| Project Started | 2021 |
| Last Release | 4 years, 3 months ago |
| Homepage | https://github.com/welch-lab/MichiGAN |
| Citations | Yu H, Welch JD, MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks., Genome Biol, May 20, 2021 [ Abstract, cited in PMC ] |
| GSR Certification | Accessibility |
| Last evaluated | Sept. 28, 2022 (934 days ago) |
| Author verification | The basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator. |
| Attribute Category | Attribute |
|---|---|
| Target | |
| Type of Simulated Data | RNA, Gene Expression, Single-Cell Sequencing, |
| Variations | |
| Simulation Method | Neural network, |
| Input | |
| Data Type | |
| File format | |
| Output | |
| Data Type | |
| Sequencing Reads | |
| File Format | |
| Sample Type | |
| Phenotype | |
| Trait Type | |
| Determinants | |
| Evolutionary Features | |
| Demographic | |
| Population Size Changes | |
| Gene Flow | |
| Spatiality | |
| Life Cycle | |
| Mating System | |
| Fecundity | |
| Natural Selection | |
| Determinant | |
| Models | |
| Recombination | |
| Mutation Models | |
| Events Allowed | |
| Other | |
| Interface | Web-based, |
| Development | |
| Tested Platforms | |
| Language | Python, |
| License | GNU Public License, |
| GSR Certification | Documentation, Support, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 1
The following 1 publications are selected examples of applications that used MichiGAN.
2022
Hazra D, Kim MR, Byun YC, Generative Adversarial Networks for Creating Synthetic Nucleic Acid Sequences of Cat Genome., Int J Mol Sci, March 28, 2022 [Abstract]