EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction - Wan, Fangping; Li, Shuya; Tian, Tingzhong; Lei, Yipin; Zhao, Dan; Zeng, Jianyang (Frontiers in Pharmacology, 2020)
Synthetic lethality (SL), an important type of genetic interactions, can provide useful insights into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in human, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network based method, called EXP2SL, to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods, and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.