Wan Fangping, Li Shuya, Tian Tingzhong, Lei Yipin, Zhao Dan, Zeng Jianyang
Institute of Interdisciplinary Information Science, Tsinghua University, Beijing, China.
Machine Learning Department, Silexon AI Technology Co. Ltd., Nanjing, China.
Front Pharmacol. 2020 Feb 28;11:112. doi: 10.3389/fphar.2020.00112. eCollection 2020.
Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight 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 humans, 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.
合成致死性（SL）是一种重要的基因相互作用类型，可为抗癌治疗药物开发的靶点识别过程提供有用的见解。尽管已经证实了几对成熟的SL基因对在人类中具有保守性，但大多数SL相互作用仍然具有细胞系特异性。在这里，我们证明了从LINCS L1000项目中进行的shRNA干扰实验获得的细胞系特异性基因表达谱可为预测人类中的SL相互作用提供有用的特征。在本文中，我们开发了一种基于半监督神经网络的方法，称为EXP2SL，以从L1000基因表达谱中准确识别SL相互作用。通过对三种不同细胞系的SL数据集进行系统评估，我们证明了我们的模型比基线方法具有更好的性能，并验证了在SL预测中使用L1000基因表达特征和半监督训练技术的有效性。
