Summary of A Multi-domain Multi-task Approach For Feature Selection From Bulk Rna Datasets, by Karim Salta et al.
A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasets
by Karim Salta, Tomojit Ghosh, Michael Kirby
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Genomics (q-bio.GN)
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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 This paper proposes a novel multi-domain, multi-task algorithm for feature selection in bulk RNAseq data. The method is applied to two datasets generated from mouse host immune responses to Salmonella infection. The data consists of samples from the spleen and liver, which serve as the two domains. Machine learning experiments are conducted, and a small subset of discriminative features that work across both domains is extracted. The algorithm demonstrates its effectiveness by extracting new features that cannot be obtained through a one-domain approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a special computer program to help scientists find important patterns in big sets of data from different parts of the body. They use this program on two large datasets that show how mice fight off salmonella infections. The data comes from different types of mouse and includes samples from their spleens and livers. By testing different approaches, they discover a small group of features that are important for understanding how the immune system works across different parts of the body. This is an important finding that could help us learn more about how our bodies work. |
Keywords
» Artificial intelligence » Feature selection » Machine learning » Multi task
