知识图及其在药物发现中的应用
1Evotec (UK) Ltd., Abingdon, Oxfordshire, UK. tim.james@evotec.com.
Methods in molecular biology (Clifton, N.J.)
|September 13, 2023
概括
知识图表组织生物医学数据,使机器学习成为药物发现洞察力的工具. 它们为可解释的AI预测提供了背景,推动了制药研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
- 药物发现 药物发现 药物发现
背景情况:
- 知识图表代表了复杂的信息作为实体和关系.
- 生物医学数据集成在可访问性和上下文化方面存在挑战.
- 可解释的预测对于当代人工智能应用至关重要.
研究的目的:
- 探索生物医学知识图的构建.
- 检查机器学习应用程序的药物发现洞察力.
- 确定生物医学知识图表开发的未来方向.
主要方法:
- 审查构建生物医学知识图表的因素.
- 分析最近在采矿知识图表系统方面的进展.
- 确定潜在的未来研究领域.
主要成果:
- 知识图表使生物医学数据的访问变得民主化.
- 他们将数据语境化和可视化,以更好地理解数据.
- 基于知识图的机器学习产生了新的药物发现洞察力.
结论:
- 生物医学知识图表是药物发现的宝贵工具.
- 它们通过上下文化数据来促进可解释的AI.
- 需要进一步开发以最大限度地发挥其潜力.
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