Indication Finding: a novel use case for representation learning
Not on the map. Why?
Abstract (arXiv)
Many therapies are effective in treating multiple diseases. We present an approach that leverages methods developed in natural language processing and real-world data to prioritize potential, new indications for a mechanism of action (MoA). We specifically use representation learning to generate embeddings of indications and prioritize them based on their proximity to the indications with the strongest available evidence for the MoA. We demonstrate the successful deployment of our approach for anti-IL-17A using embeddings generated with SPPMI and present an evaluation framework to determine the quality of indication finding results and the derived embeddings.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 49 references that are in the map. First posted on arXiv 24 Oct 2024.
Builds on
5 of 49 references.
References · 5
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2019BEHRT: Transformer for Electronic Health Recordscited by 258
- 2023The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRscited by 29
- 2022KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Descriptioncited by 4
Built on by
Not cited yet.