EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction

• Published in: Frontiers in pharmacology Vol. 11; p. 112
• Main Authors: Wan, Fangping, Li, Shuya, Tian, Tingzhong, Lei, Yipin, Zhao, Dan, Zeng, Jianyang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 28.02.2020
• Subjects: L1000 gene expression profiles; machine learning; Pharmacology; semi-supervised neural network; synthetic lethality; target identification; machine learning; L1000 gene expression profiles; synthetic lethality; target identification; semi-supervised neural network
• ISSN: 1663-9812; 1663-9812
• DOI: 10.3389/fphar.2020.00112

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.