Summary of Indication Finding: a Novel Use Case For Representation Learning, by Maren Eckhoff et al.
Indication Finding: a novel use case for representation learning
by Maren Eckhoff, Valmir Selimi, Alexander Aranovitch, Ian Lyons, Emily Briggs, Jennifer Hou, Alex Devereson, Matej Macak, David Champagne, Chris Anagnostopoulos
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 method combines natural language processing techniques and real-world data to prioritize potential new indications for a mechanism of action (MoA). By leveraging representation learning, it generates embeddings of indications and prioritizes them based on their proximity to those with strong available evidence for the MoA. The approach is demonstrated for anti-IL-17A using SPPMI-generated embeddings, along with an evaluation framework for assessing indication finding results and derived embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a new method to find potential treatments for diseases. It combines computer science techniques with real-world data to identify which disease might benefit from the same treatment that works well for another disease. This approach generates a list of potential treatments and ranks them based on how closely they match known effective treatments. The method is tested for one specific treatment (anti-IL-17A) and an evaluation system is created to measure its performance. |
Keywords
» Artificial intelligence » Natural language processing » Representation learning
