Summary of Personalised Drug Identifier For Cancer Treatment with Transformers Using Auxiliary Information, by Aishwarya Jayagopal et al.
Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information
by Aishwarya Jayagopal, Hansheng Xue, Ziyang He, Robert J. Walsh, Krishna Kumar Hariprasannan, David Shao Peng Tan, Tuan Zea Tan, Jason J. Pitt, Anand D. Jeyasekharan, Vaibhav Rajan
First submitted to arxiv on: 16 Feb 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 proposed paper develops a novel transformer-based method for accurate drug response prediction (DRP) in personalized cancer treatment strategies. The model addresses limitations in previous transfer learning approaches by explicitly modeling the sequential structure of genomic mutations and incorporating auxiliary patient survival data. This results in superior performance on benchmark datasets, outperforming state-of-the-art DRP models. Additionally, the paper presents a treatment recommendation system (TRS), currently deployed at the National University Hospital, Singapore, which is being evaluated in a clinical trial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cancer is a major global problem that affects many people. Doctors want to find a way to give each patient the right treatment, but this can be hard because every person’s cancer is different. To help with this, doctors are using genetic tests to understand what’s going on inside a patient’s body. But it’s tricky to make good predictions about how a patient will respond to treatment just by looking at their genes. The authors of this paper have come up with a new way to do this that uses special computer models and information about the patient’s outcome, like whether they survived or not. This method is better than what others have done before and could be used to help doctors make decisions about how to treat patients. |
Keywords
* Artificial intelligence * Transfer learning * Transformer
