Summary of Learning Cross-domain Representations For Transferable Drug Perturbations on Single-cell Transcriptional Responses, by Hui Liu and Shikai Jin
Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses
by Hui Liu, Shikai Jin
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel generative framework called XTransferCDR is proposed for feature decoupling and transferable representation learning across domains in the context of phenotypic drug discovery. The approach utilizes domain separation encoders to separate perturbation representations from basal states, followed by cross-transfer in the latent space. This process promotes the learning of transferable drug perturbation representations, which are then used to reconstruct perturbed expression profiles via a shared decoder. Evaluations on multiple datasets demonstrate that XTransferCDR outperforms current state-of-the-art methods, highlighting its potential to advance phenotypic drug discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Phenotypic drug discovery is trying to find new medicines by looking at how cells respond to different treatments. Scientists use special tools called transcriptomic profiling to understand these responses. A new way of doing this, called XTransferCDR, helps learn patterns in cell behavior that can be applied to different situations. This could lead to better ways of finding new drugs and understanding how they work. |
Keywords
» Artificial intelligence » Decoder » Latent space » Representation learning
