Summary of Low Dimensional Representation Of Multi-patient Flow Cytometry Datasets Using Optimal Transport For Minimal Residual Disease Detection in Leukemia, by Erell Gachon et al.
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemia
by Erell Gachon, Jérémie Bigot, Elsa Cazelles, Audrey Bidet, Jean-Philippe Vial, Pierre-Yves Dumas, Aguirre Mimoun
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
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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 novel statistical learning methods based on optimal transport (OT) to represent and quantify Minimal Residual Disease (MRD) in Acute Myeloid Leukemia (AML). The authors aim to improve the prognosis and follow-up of AML patients by analyzing flow cytometry datasets. They apply OT-based techniques, including K-means algorithm for dimensionality reduction and Wasserstein Principal Component Analysis (PCA) or log-ratio PCA for embedding high-dimensional probability distributions into a linear space. This approach outperforms the kernel mean embedding technique in terms of statistical learning from multiple high-dimensional probability distributions. The authors demonstrate the effectiveness of their methodology using publicly available datasets, including a FCM dataset from Bordeaux University Hospital. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better understand and track Acute Myeloid Leukemia (AML), a type of cancer that affects the blood and bone marrow. Right now, it’s hard to detect tiny amounts of leukemia cells in patients’ blood samples. The researchers came up with new ways to analyze these samples using special mathematical techniques called optimal transport. They tested their methods on real data from hospitals and showed they can work better than other approaches. This could lead to more accurate diagnoses and treatment plans for AML patients. |
Keywords
* Artificial intelligence * Dimensionality reduction * Embedding * K means * Pca * Principal component analysis * Probability
