Summary of Distance-preserving Spatial Representations in Genomic Data, by Wenbin Zhou and Jin-hong Du
Distance-Preserving Spatial Representations in Genomic Data
by Wenbin Zhou, Jin-Hong Du
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called dp-VAE for reconstructing spatial context from single-cell gene expression data. The model uses a distance-preserving regularizer to capture spatial context signals from reference datasets, enabling the reconstruction or imputation of missing spatial information. The authors explore theoretical connections between distance preservation and generative models, demonstrating the effectiveness of dp-VAE in various tasks such as training robustness, out-of-sample evaluation, and transfer learning inference using 27 publicly available datasets. Key phrases include spatial context, single-cell gene expression, dp-VAE, distance-preserving regularizer, and genomics studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists often struggle to understand how genes work together in different parts of the body because they don’t have enough information about where those genes are located. This paper presents a new way to use computer models to learn more about this spatial context from existing data. The model is able to fill in missing information and make predictions about gene expression based on what it has learned. The authors tested their method using 27 different datasets and showed that it can be useful for many types of genetic studies. |
Keywords
* Artificial intelligence * Inference * Transfer learning
