TarGo: network based target gene selection system for human disease related mouse models

Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “T...
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Autor*in:	Hyung, Daejin [verfasserIn] Mallon, Ann-Marie Kyung, Dong Soo Cho, Soo Young Seong, Je Kyung

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Systems biology Genetic engineered mice Bioinformatics PageRank algorithm Database

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: Laboratory Animal Research - Seoul, 2010, 35(2019), 1 vom: 13. Nov.
Übergeordnetes Werk:	volume:35 ; year:2019 ; number:1 ; day:13 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s42826-019-0023-z

Katalog-ID:	SPR038689456

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allfields	10.1186/s42826-019-0023-z doi (DE-627)SPR038689456 (SPR)s42826-019-0023-z-e DE-627 ger DE-627 rakwb eng Hyung, Daejin verfasserin aut TarGo: network based target gene selection system for human disease related mouse models 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. Systems biology (dpeaa)DE-He213 Genetic engineered mice (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 PageRank algorithm (dpeaa)DE-He213 Database (dpeaa)DE-He213 Mallon, Ann-Marie aut Kyung, Dong Soo aut Cho, Soo Young aut Seong, Je Kyung aut Enthalten in Laboratory Animal Research Seoul, 2010 35(2019), 1 vom: 13. Nov. (DE-627)666217815 (DE-600)2623220-0 2233-7660 nnns volume:35 year:2019 number:1 day:13 month:11 https://dx.doi.org/10.1186/s42826-019-0023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2019 1 13 11
spelling	10.1186/s42826-019-0023-z doi (DE-627)SPR038689456 (SPR)s42826-019-0023-z-e DE-627 ger DE-627 rakwb eng Hyung, Daejin verfasserin aut TarGo: network based target gene selection system for human disease related mouse models 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. Systems biology (dpeaa)DE-He213 Genetic engineered mice (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 PageRank algorithm (dpeaa)DE-He213 Database (dpeaa)DE-He213 Mallon, Ann-Marie aut Kyung, Dong Soo aut Cho, Soo Young aut Seong, Je Kyung aut Enthalten in Laboratory Animal Research Seoul, 2010 35(2019), 1 vom: 13. Nov. (DE-627)666217815 (DE-600)2623220-0 2233-7660 nnns volume:35 year:2019 number:1 day:13 month:11 https://dx.doi.org/10.1186/s42826-019-0023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2019 1 13 11
allfields_unstemmed	10.1186/s42826-019-0023-z doi (DE-627)SPR038689456 (SPR)s42826-019-0023-z-e DE-627 ger DE-627 rakwb eng Hyung, Daejin verfasserin aut TarGo: network based target gene selection system for human disease related mouse models 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. Systems biology (dpeaa)DE-He213 Genetic engineered mice (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 PageRank algorithm (dpeaa)DE-He213 Database (dpeaa)DE-He213 Mallon, Ann-Marie aut Kyung, Dong Soo aut Cho, Soo Young aut Seong, Je Kyung aut Enthalten in Laboratory Animal Research Seoul, 2010 35(2019), 1 vom: 13. Nov. (DE-627)666217815 (DE-600)2623220-0 2233-7660 nnns volume:35 year:2019 number:1 day:13 month:11 https://dx.doi.org/10.1186/s42826-019-0023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2019 1 13 11
allfieldsGer	10.1186/s42826-019-0023-z doi (DE-627)SPR038689456 (SPR)s42826-019-0023-z-e DE-627 ger DE-627 rakwb eng Hyung, Daejin verfasserin aut TarGo: network based target gene selection system for human disease related mouse models 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. Systems biology (dpeaa)DE-He213 Genetic engineered mice (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 PageRank algorithm (dpeaa)DE-He213 Database (dpeaa)DE-He213 Mallon, Ann-Marie aut Kyung, Dong Soo aut Cho, Soo Young aut Seong, Je Kyung aut Enthalten in Laboratory Animal Research Seoul, 2010 35(2019), 1 vom: 13. Nov. (DE-627)666217815 (DE-600)2623220-0 2233-7660 nnns volume:35 year:2019 number:1 day:13 month:11 https://dx.doi.org/10.1186/s42826-019-0023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2019 1 13 11
allfieldsSound	10.1186/s42826-019-0023-z doi (DE-627)SPR038689456 (SPR)s42826-019-0023-z-e DE-627 ger DE-627 rakwb eng Hyung, Daejin verfasserin aut TarGo: network based target gene selection system for human disease related mouse models 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. Systems biology (dpeaa)DE-He213 Genetic engineered mice (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 PageRank algorithm (dpeaa)DE-He213 Database (dpeaa)DE-He213 Mallon, Ann-Marie aut Kyung, Dong Soo aut Cho, Soo Young aut Seong, Je Kyung aut Enthalten in Laboratory Animal Research Seoul, 2010 35(2019), 1 vom: 13. Nov. (DE-627)666217815 (DE-600)2623220-0 2233-7660 nnns volume:35 year:2019 number:1 day:13 month:11 https://dx.doi.org/10.1186/s42826-019-0023-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2019 1 13 11
language	English
source	Enthalten in Laboratory Animal Research 35(2019), 1 vom: 13. Nov. volume:35 year:2019 number:1 day:13 month:11
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author	Hyung, Daejin
spellingShingle	Hyung, Daejin misc Systems biology misc Genetic engineered mice misc Bioinformatics misc PageRank algorithm misc Database TarGo: network based target gene selection system for human disease related mouse models
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topic_title	TarGo: network based target gene selection system for human disease related mouse models Systems biology (dpeaa)DE-He213 Genetic engineered mice (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 PageRank algorithm (dpeaa)DE-He213 Database (dpeaa)DE-He213
topic	misc Systems biology misc Genetic engineered mice misc Bioinformatics misc PageRank algorithm misc Database
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title	TarGo: network based target gene selection system for human disease related mouse models
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title_full	TarGo: network based target gene selection system for human disease related mouse models
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author_browse	Hyung, Daejin Mallon, Ann-Marie Kyung, Dong Soo Cho, Soo Young Seong, Je Kyung
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author-letter	Hyung, Daejin
doi_str_mv	10.1186/s42826-019-0023-z
title_sort	targo: network based target gene selection system for human disease related mouse models
title_auth	TarGo: network based target gene selection system for human disease related mouse models
abstract	Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. © The Author(s) 2019
abstractGer	Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. © The Author(s) 2019
abstract_unstemmed	Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction. © The Author(s) 2019
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title_short	TarGo: network based target gene selection system for human disease related mouse models
url	https://dx.doi.org/10.1186/s42826-019-0023-z
remote_bool	true
author2	Mallon, Ann-Marie Kyung, Dong Soo Cho, Soo Young Seong, Je Kyung
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up_date	2024-07-03T19:28:33.226Z
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