CRISPR-GEM:一种用于CRISPR遗传目标发现和评估的新型机器学习模型
Josh P Graham1, Yu Zhang1,2, Lifang He3
1Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
bioRxiv : the preprint server for biology
|July 15, 2024
概括
新的机器学习模型CRISPR-GEM通过预测基因调节网络效应来确定最佳的CRISPR基因编辑目标. 这通过改善基因标选择以提高安全性和有效性来推进细胞疗法.
科学领域:
- 基因组学和生物信息学
- 基因编辑技术的技术
- 机器学习在生物学中的应用
背景情况:
- 克里斯普尔基因编辑为细胞治疗提供了对基因表达的精确控制.
- 有效的CRISPR策略取决于在复杂的基因调节网络 (GRNs) 中选择目标基因.
- 现有的GRN重建方法仅限于单细胞类型和转录因子,忽视了许多潜在的CRISPR目标.
研究的目的:
- 开发一种新的机器学习模型,CRISPR-GEM,用于预测CRISPR基因编辑的下游效应.
- 通过结合不同基因类型超越转录因子来克服当前GRN模型的局限性.
- 为了确定最优的CRISPR目标基因,有效调节GRN向所需的细胞表型.
主要方法:
- 开发了一个基于多层感知子 (MLP) 的合成GRN模型 (CRISPR-GEM).
- 输入和输出节点被确定为实验和目标细胞类型之间的差异表达基因.
- 训练有素的MLP学习调节关系,预测CRISPR模拟性扰动后的基因表达变化.
主要成果:
- CRISPR-GEM准确地预测了CRISPR基因编辑的下游影响.
- 该模型确定了最高得分的基因,这些基因最能调节GRN,以实现目标转录基因转移.
- 这种方法可以对更广泛的潜在CRISPR基因进行评估.
结论:
- CRISPR-GEM是第一个设计用于预测最佳CRISPR目标基因的机器学习模型.
- 该工具通过为细胞疗法提供知情的基因选择来增强CRISPR策略.
- 该模型通过优化基因编辑目标,促进了治疗应用中的安全性和疗效的提高.
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