SLOAD

First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing.

首先，基于已收集的携带合成致死相互作用（synthetic lethal interaction）的公开基因对，研究团队通过整合多组学数据（multi-omics data）——主要涵盖DNA突变、拷贝数变异、甲基化与mRNA表达数据——对候选基因对开展分析。随后，研究人员采用随机森林算法，基于整合得到的特征预测癌症特异性合成致死相互作用。最终，本研究整合上述研究成果，构建了SLOAD数据库（访问网址：http://www.tmliang.cn/SLOAD），该数据库界面友好，支持数据检索、浏览、下载与分析操作。