对CTEPH药物的探索:结合自然语言处理,嵌入式学习,体外模型和药物重定向的现实世界的证据
Daniel Steiert1, Corey Wittig1, Priyanka Banerjee2
1Laboratory of in vitro modeling systems of pulmonary and thrombotic diseases, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
PLoS computational biology
|September 12, 2024
概括
自然语言处理 (NLP) 确定了慢性血栓栓塞性肺高血压 (CTEPH) 药物重定位的阿米奥达龙. 然而,临床数据显示死亡风险增加,突出显示在罕见疾病研究中需要综合证据.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 管理大量的科学文献对研究人员来说是一个挑战.
- 在多个学科的进步需要保持知情.
研究的目的:
- 开发PubDigest,一个使用NLP和嵌入学习的工具.
- 从PubMed文献中识别潜在的药物重用候选人.
主要方法:
- 应用NLP,特别是词嵌入和术语关联.
- 专注于慢性血栓栓塞性肺高血压 (CTEPH) 和相关文献.
- 利用了TF-IDF评分和神经网络上下文化.
主要成果:
- 鉴定了静脉血栓症 (VT) 作为与CTEPH相关的疾病.
- 发现了福衍生物,包括阿米奥达龙,作为潜在的CTEPH治疗方法.
- 在体外证实了阿米奥达龙的抗血小板聚合 (68%的减少).
- 发现阿米奥达龙与CTEPH患者的死亡风险增加15.9%有关.
结论:
- 在科学文献中,NLP对药物重用有价值.
- 将NLP与体外和现实世界的数据结合起来,对于药物重新定位至关重要.
- 这种综合方法有助于更快,更安全的药物发现,用于罕见疾病,如CTEPH.
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