Summary of Wiser: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer, by Kumar Shubham et al.
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer
by Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh AP, Vaibhav Rajan
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This study aims to improve personalized cancer treatment by developing a machine learning model that can predict drug response in patients. The authors focus on addressing the limitations of current methods, which often rely on unsupervised domain-invariant representation learning and downstream classification steps. They propose a novel approach that incorporates supervision in both stages and is more effective at predicting personalized drug response. The method, called WISER, demonstrates improved performance compared to state-of-the-art alternatives when evaluated on real patient data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to help make cancer treatment better by using computers to predict how people will react to medicines. Right now, we can’t do this very well because we don’t have enough information about how people’s bodies respond to different drugs. The researchers found a way to improve this prediction method and tested it on real patient data. Their new approach worked better than other methods they tried. |
Keywords
» Artificial intelligence » Classification » Machine learning » Representation learning » Unsupervised
