用表示学习发现药物新适应症，靠相似性排序提高发现效率。
Indication Finding: a novel use case for representation learning
- 用SPPMI生成疾病嵌入，基于语义相似性排序潜在适应症。
- 在抗IL-17A药物上验证，成功识别出已有证据支持的适应症。
- 提供评估框架，可衡量结果质量与嵌入有效性，适合药物研发人员。
许多疗法对多种疾病有效。本文提出一种新方法，利用自然语言处理技术与真实世界数据，优先筛选机制作用（MoA）的潜在新适应症。通过表示学习生成疾病嵌入，根据其与已有强证据适应症的距离进行排序。以抗IL-17A为例，使用SPPMI生成的嵌入成功实现适应症推荐，并构建评估框架，用于判断指示发现结果与嵌入质量。
原文摘要 · Abstract (English)
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.
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