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

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...

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Main Authors: HAO, Zhifeng, WU, Di, FANG, Yuan, WU, Min, CAI, Ruichu, LI, Xiaoli
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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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
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spelling sg-smu-ink.sis_research-77452022-01-27T10:52:05Z Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder HAO, Zhifeng WU, Di FANG, Yuan WU, Min CAI, Ruichu LI, Xiaoli 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. 2021-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6742 info:doi/10.1109/JBHI.2021.3079302 https://ink.library.smu.edu.sg/context/sis_research/article/7745/viewcontent/JBHI21_SLMGAE.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Synthetic lethality graph neural network graph auto-encoder multi-view human cancers Databases and Information Systems Graphics and Human Computer Interfaces Health Information Technology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Synthetic lethality
graph neural network
graph auto-encoder
multi-view
human cancers
Databases and Information Systems
Graphics and Human Computer Interfaces
Health Information Technology
spellingShingle Synthetic lethality
graph neural network
graph auto-encoder
multi-view
human cancers
Databases and Information Systems
Graphics and Human Computer Interfaces
Health Information Technology
HAO, Zhifeng
WU, Di
FANG, Yuan
WU, Min
CAI, Ruichu
LI, Xiaoli
Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
description 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.
format text
author HAO, Zhifeng
WU, Di
FANG, Yuan
WU, Min
CAI, Ruichu
LI, Xiaoli
author_facet HAO, Zhifeng
WU, Di
FANG, Yuan
WU, Min
CAI, Ruichu
LI, Xiaoli
author_sort HAO, Zhifeng
title Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
title_short Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
title_full Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
title_fullStr Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
title_full_unstemmed Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
title_sort prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6742
https://ink.library.smu.edu.sg/context/sis_research/article/7745/viewcontent/JBHI21_SLMGAE.pdf
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