SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

Zhu, Yan; Zhou, Yuhuan; Liu, Yang; Wang, Xuan; Li, Junyi

doi:10.1093/bioinformatics/btad015

Journal article 26 views 9 downloads

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

Yan Zhu, Yuhuan Zhou, Yang Liu

, Xuan Wang, Junyi Li

Bioinformatics, Volume: 39, Issue: 2

Swansea University Author: Yang Liu

PDF | Version of Record

Copyright: The Authors 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.
Download (1.9MB)

Check full text

DOI (Published version): 10.1093/bioinformatics/btad015

Abstract

MotivationSynthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL...

Full description

Published in:	Bioinformatics
ISSN:	1367-4811
Published:	Oxford University Press (OUP) 2023
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa67389
Tags:	Add Tag No Tags, Be the first to tag this record!

first_indexed	2024-09-20T13:39:34Z
last_indexed	2024-09-20T13:39:34Z
id	cronfa67389
recordtype	SURis
fullrecord	<?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>67389</id><entry>2024-08-15</entry><title>SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network</title><swanseaauthors><author><sid>ba37dab58c9093dc63c79001565b75d4</sid><ORCID>0000-0003-2486-5765</ORCID><firstname>Yang</firstname><surname>Liu</surname><name>Yang Liu</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-08-15</date><deptcode>MACS</deptcode><abstract>MotivationSynthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed.ResultsIn this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability.</abstract><type>Journal Article</type><journal>Bioinformatics</journal><volume>39</volume><journalNumber>2</journalNumber><paginationStart/><paginationEnd/><publisher>Oxford University Press (OUP)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1367-4811</issnElectronic><keywords/><publishedDay>8</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-02-08</publishedDate><doi>10.1093/bioinformatics/btad015</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This work was supported by the grants from the National Key R&D Program of China [2021YFA0910700]; Shenzhen Science and Technology University Stable Support Program [GXWD20201230155427003-20200821222112001]; Shenzhen Science and Technology Program [JCYJ20200109113201726]; Guangdong Basic and Applied Basic Research Foundation [2021A1515012461, 2021A1515220115]; and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies [2022B1212010005].</funders><projectreference/><lastEdited>2024-09-20T14:42:25.5376487</lastEdited><Created>2024-08-15T16:59:27.5662908</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Yan</firstname><surname>Zhu</surname><order>1</order></author><author><firstname>Yuhuan</firstname><surname>Zhou</surname><order>2</order></author><author><firstname>Yang</firstname><surname>Liu</surname><orcid>0000-0003-2486-5765</orcid><order>3</order></author><author><firstname>Xuan</firstname><surname>Wang</surname><order>4</order></author><author><firstname>Junyi</firstname><surname>Li</surname><orcid>0000-0001-8045-5264</orcid><order>5</order></author></authors><documents><document><filename>67389__31419__1a537708829a4d5c9ddb7bbf638669a4.pdf</filename><originalFilename>67389.VoR.pdf</originalFilename><uploaded>2024-09-20T14:40:09.0490809</uploaded><type>Output</type><contentLength>1987334</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: The Authors 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/)</licence></document></documents><OutputDurs/></rfc1807>
spelling	v2 67389 2024-08-15 SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network ba37dab58c9093dc63c79001565b75d4 0000-0003-2486-5765 Yang Liu Yang Liu true false 2024-08-15 MACS MotivationSynthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed.ResultsIn this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability. Journal Article Bioinformatics 39 2 Oxford University Press (OUP) 1367-4811 8 2 2023 2023-02-08 10.1093/bioinformatics/btad015 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This work was supported by the grants from the National Key R&D Program of China [2021YFA0910700]; Shenzhen Science and Technology University Stable Support Program [GXWD20201230155427003-20200821222112001]; Shenzhen Science and Technology Program [JCYJ20200109113201726]; Guangdong Basic and Applied Basic Research Foundation [2021A1515012461, 2021A1515220115]; and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies [2022B1212010005]. 2024-09-20T14:42:25.5376487 2024-08-15T16:59:27.5662908 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Yan Zhu 1 Yuhuan Zhou 2 Yang Liu 0000-0003-2486-5765 3 Xuan Wang 4 Junyi Li 0000-0001-8045-5264 5 67389__31419__1a537708829a4d5c9ddb7bbf638669a4.pdf 67389.VoR.pdf 2024-09-20T14:40:09.0490809 Output 1987334 application/pdf Version of Record true Copyright: The Authors 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng https://creativecommons.org/licenses/by/4.0/)
title	SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network
spellingShingle	SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network Yang Liu
title_short	SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network
title_full	SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network
title_fullStr	SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network
title_full_unstemmed	SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network
title_sort	SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network
author_id_str_mv	ba37dab58c9093dc63c79001565b75d4
author_id_fullname_str_mv	ba37dab58c9093dc63c79001565b75d4_***_Yang Liu
author	Yang Liu
author2	Yan Zhu Yuhuan Zhou Yang Liu Xuan Wang Junyi Li
format	Journal article
container_title	Bioinformatics
container_volume	39
container_issue	2
publishDate	2023
institution	Swansea University
issn	1367-4811
doi_str_mv	10.1093/bioinformatics/btad015
publisher	Oxford University Press (OUP)
college_str	Faculty of Science and Engineering
hierarchytype
hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str	1
active_str	0
description	MotivationSynthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed.ResultsIn this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability.
published_date	2023-02-08T14:42:24Z
_version_	1810722711470604288
score	11.028798

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

Similar Items