Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder

• Main Authors: HAO, Zhifeng, WU, Di, FANG, Yuan, WU, Min, CAI, Ruichu, LI, Xiaoli
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Synthetic lethality; graph neural network; graph auto-encoder; multi-view; human cancers; Databases and Information Systems; Graphics and Human Computer Interfaces; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/6742; https://ink.library.smu.edu.sg/context/sis_research/article/7745/viewcontent/JBHI21_SLMGAE.pdf
• Institution: Singapore Management University

Synthetic lethality (SL) is a very important concept for the development of targeted anticancer drugs. However, experimental methods for SL detection often suffer from various issues like high cost and low consistency across cell lines. Hence, computational methods for predicting novel SLs have rece...