基于斯旺森框架和生物信息学的疾病和药物之间的潜在关联预测的探索和实践
Yanhua Lv1, Yuyang Yuan2, Xiaoyun Zhong2
1Shanxi Medical University, Jinzhong, China. lvyanhua01@163.com.
Scientific reports
|November 28, 2024
概括
这项研究引入了一种新的"斯旺森框架+生物信息学"方法,以增强基于文学的知识发现. BIOINF-ABC+模型准确地预测了潜在的疾病药物关联,以识别自闭症谱系障碍的鱼油和福斯科林为例.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 知识 发现 发现 发现
背景情况:
- 基于文学知识发现 (LKD) 的传统中间概念缺乏信息性和方向性.
- 提高LKD的效率和科学严谨性对于研发至关重要.
研究的目的:
- 提出和验证一个小说.
主要方法:
- 基于文献的知识发现 (LKD)
- 基因功能分析以确定与疾病相关的基因作为中间概念.
- BIOINF-ABC+模型整合了斯旺森框架和生物信息学方法.
主要成果:
- 确定了自闭症谱系障碍 (ASD) 的潜在"疾病药物"关联.
- 通过生物信息学分析和文献验证,发现鱼油和 Forskolin 可能与 ASD 有关.
- 与单独的BITOLA系统相比,BIOINF-ABC+模型在计算准确度上有76%的改进.
结论:
- 通过BIOINF-ABC+模型实施的"斯旺森框架+生物信息学"方法显著提高了LKD的科学性和效率.
- 该模型显示了高的实用性,适用性和准确性,用于生物医学中的"疾病-药物"关联预测.
- 这种方法提供了一个可靠的工具,用于挖掘新的"疾病-药物"关系.
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