Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2021

Abstract

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 recently emerged as complements for wet-lab experiments. In addition, SL data can be represented as a graph where nodes are genes and edges are the SL interactions. It is thus motivated to design advanced graph-based machine learning algorithms for SL prediction. In this paper, we propose a novel SL prediction method using Multi-view Graph Auto-Encoder (SLMGAE). We consider the SL graph as the main view and the graphs from other data sources (e.g., PPI, GO, etc.) as support views. Multiple Graph Auto-Encoders (GAEs) are implemented to reconstruct the graphs for different views. We further design an attention mechanism, which assigns different weights for support views, to combine all the reconstructed graphs for SL prediction. The overall SLMGAE model is then trained by minimizing both the reconstruction error and prediction error. Experimental results on the SynLethDB dataset show that SLMGAE outperforms state-of-the-arts. The case studies on novel predicted SLs also illustrate the effectiveness of our SLMGAE method.

Keywords

Synthetic lethality, graph neural network, graph auto-encoder, multi-view, human cancers

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces | Health Information Technology

Research Areas

Data Science and Engineering

Publication

IEEE Journal of Biomedical and Health Informatics

Volume

25

Issue

10

First Page

4041

Last Page

4051

ISSN

2168-2194

Identifier

10.1109/JBHI.2021.3079302

Publisher

Institute of Electrical and Electronics Engineers

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