BiGSM:通过稀疏建模对基因调节网络的贝叶斯推断
Hang Qin1, Mateusz Garbulowski2,3, Erik L L Sonnhammer2
1Digital Futures, and School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm 11428, Sweden.
Bioinformatics (Oxford, England)
|June 9, 2025
概括
我们开发了BiGSM,这是一种贝叶斯基因调节网络推断的贝叶斯方法. 它准确地从噪音数据中识别基因链接,并提供信心水平,优于现有方法.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因调节网络 (GRN) 的推断由于稀疏的矩阵和杂的表达数据而复杂,导致预测不准确.
- 现有的方法往往提供固定的估计,限制了全面的网络分析和信任评估.
- 贝叶斯式方法提供了概率链接选择和统计信心见解,对于强大的GRN推理至关重要.
研究的目的:
- 开发一个强大的贝叶斯方法,用于基因调节网络推断,处理数据稀疏性和噪声.
- 创建一种提供概率链接选择和量化对预测GRN链接的信心的方法.
- 严格对拟议的方法与最先进的GRN推断技术进行比较.
主要方法:
- 通过Sparse Modelling (BiGSM) 对GRN的建议贝叶斯推断.
- 利用基于最大概率的学习,从噪音表达数据推断GRN链接的后部分布.
- 提高了GRN矩阵稀疏性,以提高推理准确度.
主要成果:
- 在基准测试中,BiGSM在各种噪声级别和数据模型中在准确性和稳定性方面表现出卓越的表现.
- 超越了使用点估计措施的GENIE3,LASSO,LSCON和Zscore等最先进的方法.
- BiGSM独特地为GRN权重提供后置概率,使每个预测的链接能够进行信心评估.
结论:
- BiGSM为基因调控网络推断提供了强大的和信息化的方法.
- 该方法提供概率输出的能力提高了推断的GRNs的可靠性和可解释性.
- BiGSM代表了系统生物学研究计算方法的重大进步.
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