合成致命连接和图形变压器改善了合成致命性预测
Kunjie Fan1, Birkan Gökbağ1, Shan Tang2
1Department of Biomedical Informatics, College of Medicine, The Ohio State University, 1800 Cannon Drive, Columbus, OH 43210, United States.
Briefings in bioinformatics
|August 29, 2024
概括
合成死亡率 (SL) 预测模型对于癌症点发现至关重要. 一个新的多层编码器 (MLEC-iSL) 有效地预测SL相互作用,改善了CRISPR双击实验的基因对选择.
科学领域:
- 计算生物学和生物信息学
- 基因组学和癌症研究
背景情况:
- 合成致死性 (SL) 是一种有前途的策略,用于识别新的癌症点.
- 目前的CRISPR双击 (CDKO) 技术在全基因组查能力方面是有限的.
- 缺少可扩展和通用化的SL属性,阻碍了预测建模工作.
研究的目的:
- 开发一个强大的,可扩展和可通用的SL预测模型.
- 改进用于CDKO实验的基因对的选择.
- 为了确定癌症治疗的新型SL相互作用.
主要方法:
- 开发了一种新的两步多层编码器,用于单个样本特定的SL预测 (MLEC-iSL).
- MLEC-iSL集成了基因,图形和变压器编码器,以预测SL连接和相互作用.
- 使用K562和Jurkat细胞系和22Rv1细胞中的CDKO实验验验证了MLEC-iSL性能.
主要成果:
- 在K562 (AUPR 0.73,AUC 0.72) 和Jurkat (AUPR 0.73,AUC 0.71) 细胞中,MLEC-iSL实现了高预测性能,优于现有方法.
- 在22Rv1细胞的实验验证显示,在987个选择的基因对中,SL率为46.8%.
- 这项研究揭示了细胞亡和细胞分裂细胞死亡途径之间的SL依赖性.
结论:
- SL连接是一种适合预测建模的可扩展和通用性质.
- 在预测SL相互作用和指导CDKO实验方面,MLEC-iSL表现出卓越的性能.
- 这些发现突出了通过合成致死性在癌症中潜在的治疗点和途径.
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