Summary of Interpreting Microbiome Relative Abundance Data Using Symbolic Regression, by Swagatam Haldar et al.
Interpreting Microbiome Relative Abundance Data Using Symbolic Regression
by Swagatam Haldar, Christoph Stein-Thoeringer, Vadim Borisov
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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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 explores the application of symbolic regression (SR) to microbiome relative abundance data, focusing on colorectal cancer (CRC). SR is compared against traditional machine learning models like random forest and gradient boosting decision trees. The evaluation metrics include F1 score and accuracy. The study uses 71 studies from various cohorts, encompassing over 10,000 samples across 749 species features. The results show that SR not only performs reasonably well in terms of predictive performance but also excels in model interpretability. SR provides explicit mathematical expressions offering insights into biological relationships within the microbiome, a crucial advantage for clinical and biological interpretation. Additionally, SR can help understand complex models like XGBoost via knowledge distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses machine learning to better understand the microbiome, which is important for developing new treatments and diagnostic tools. The researchers compare different machine learning models to see how well they work. They look at a big dataset with information from many studies and thousands of samples. The results show that one type of model called symbolic regression is really good at explaining what it’s doing, which can be helpful for doctors and scientists trying to understand the microbiome. |
Keywords
» Artificial intelligence » Boosting » F1 score » Knowledge distillation » Machine learning » Random forest » Regression » Xgboost
