Summary of Pruning Neural Network Models For Gene Regulatory Dynamics Using Data and Domain Knowledge, by Intekhab Hossain et al.
Pruning neural network models for gene regulatory dynamics using data and domain knowledge
by Intekhab Hossain, Jonas Fischer, Rebekka Burkholz, John Quackenbush
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM); Applications (stat.AP); 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 This paper presents a framework called DASH for improving the interpretability of neural networks in scientific discovery. It addresses a common limitation of state-of-the-art techniques in identifying biologically meaningful structures within gene regulatory networks. The authors propose using domain-specific structural information to guide pruning and achieve sparser, more robust models that are better interpretable. They demonstrate the effectiveness of DASH on both synthetic data and real-world gene expression data, outperforming general pruning methods by a large margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models easier to understand in science. It’s like trying to solve a puzzle, but with lots of pieces that don’t quite fit together yet. The researchers found that most models are not very good at discovering new things because they don’t take into account what we already know about the subject. They created a new way called DASH to make models simpler and more useful for scientists by using information from biology. This helps them find better answers and understand how things work. |
Keywords
* Artificial intelligence * Machine learning * Pruning * Synthetic data
