An

Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis an...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiao, Qiu [verfasserIn] Yu, Haiming [verfasserIn] Zhong, Jiancheng [verfasserIn] Liang, Cheng [verfasserIn] Li, Guanghui [verfasserIn] Ding, Pingjian [verfasserIn] Luo, Jiawei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning

Übergeordnetes Werk:	Enthalten in: Genomics - San Diego, Calif. : Academic Press, 1987, 112, Seite 3407-3415
Übergeordnetes Werk:	volume:112 ; pages:3407-3415

DOI / URN:	10.1016/j.ygeno.2020.06.017

Katalog-ID:	ELV004373006

Internformat


LEADER	01000caa a22002652 4500
001	ELV004373006
003	DE-627
005	20230524161321.0
007	cr uuu---uuuuu
008	230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.ygeno.2020.06.017 \|2 doi
035			\|a (DE-627)ELV004373006
035			\|a (ELSEVIER)S0888-7543(20)30505-X
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 610 \|a 570 \|q DE-600
084			\|a 42.20 \|2 bkl
084			\|a 44.48 \|2 bkl
100	1		\|a Xiao, Qiu \|e verfasserin \|4 aut
245	1	0	\|a An
264		1	\|c 2020
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.
650		4	\|a Circular RNA (circRNA)
650		4	\|a Disease circRNA prediction
650		4	\|a circRNA-disease network
650		4	\|a Multi-label learning
700	1		\|a Yu, Haiming \|e verfasserin \|4 aut
700	1		\|a Zhong, Jiancheng \|e verfasserin \|4 aut
700	1		\|a Liang, Cheng \|e verfasserin \|4 aut
700	1		\|a Li, Guanghui \|e verfasserin \|4 aut
700	1		\|a Ding, Pingjian \|e verfasserin \|4 aut
700	1		\|a Luo, Jiawei \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Genomics \|d San Diego, Calif. : Academic Press, 1987 \|g 112, Seite 3407-3415 \|h Online-Ressource \|w (DE-627)266881815 \|w (DE-600)1468023-3 \|w (DE-576)078707269 \|x 1089-8646 \|7 nnns
773	1	8	\|g volume:112 \|g pages:3407-3415
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_187
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
936	b	k	\|a 42.20 \|j Genetik
936	b	k	\|a 44.48 \|j Medizinische Genetik
951			\|a AR
952			\|d 112 \|h 3407-3415

Indexfelder

author_variant	q x qx h y hy j z jz c l cl g l gl p d pd j l jl
matchkey_str	article:10898646:2020----::
hierarchy_sort_str	2020
bklnumber	42.20 44.48
publishDate	2020
allfields	10.1016/j.ygeno.2020.06.017 doi (DE-627)ELV004373006 (ELSEVIER)S0888-7543(20)30505-X DE-627 ger DE-627 rda eng 610 570 DE-600 42.20 bkl 44.48 bkl Xiao, Qiu verfasserin aut An 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy. Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning Yu, Haiming verfasserin aut Zhong, Jiancheng verfasserin aut Liang, Cheng verfasserin aut Li, Guanghui verfasserin aut Ding, Pingjian verfasserin aut Luo, Jiawei verfasserin aut Enthalten in Genomics San Diego, Calif. : Academic Press, 1987 112, Seite 3407-3415 Online-Ressource (DE-627)266881815 (DE-600)1468023-3 (DE-576)078707269 1089-8646 nnns volume:112 pages:3407-3415 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 Genetik 44.48 Medizinische Genetik AR 112 3407-3415
spelling	10.1016/j.ygeno.2020.06.017 doi (DE-627)ELV004373006 (ELSEVIER)S0888-7543(20)30505-X DE-627 ger DE-627 rda eng 610 570 DE-600 42.20 bkl 44.48 bkl Xiao, Qiu verfasserin aut An 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy. Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning Yu, Haiming verfasserin aut Zhong, Jiancheng verfasserin aut Liang, Cheng verfasserin aut Li, Guanghui verfasserin aut Ding, Pingjian verfasserin aut Luo, Jiawei verfasserin aut Enthalten in Genomics San Diego, Calif. : Academic Press, 1987 112, Seite 3407-3415 Online-Ressource (DE-627)266881815 (DE-600)1468023-3 (DE-576)078707269 1089-8646 nnns volume:112 pages:3407-3415 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 Genetik 44.48 Medizinische Genetik AR 112 3407-3415
allfields_unstemmed	10.1016/j.ygeno.2020.06.017 doi (DE-627)ELV004373006 (ELSEVIER)S0888-7543(20)30505-X DE-627 ger DE-627 rda eng 610 570 DE-600 42.20 bkl 44.48 bkl Xiao, Qiu verfasserin aut An 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy. Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning Yu, Haiming verfasserin aut Zhong, Jiancheng verfasserin aut Liang, Cheng verfasserin aut Li, Guanghui verfasserin aut Ding, Pingjian verfasserin aut Luo, Jiawei verfasserin aut Enthalten in Genomics San Diego, Calif. : Academic Press, 1987 112, Seite 3407-3415 Online-Ressource (DE-627)266881815 (DE-600)1468023-3 (DE-576)078707269 1089-8646 nnns volume:112 pages:3407-3415 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 Genetik 44.48 Medizinische Genetik AR 112 3407-3415
allfieldsGer	10.1016/j.ygeno.2020.06.017 doi (DE-627)ELV004373006 (ELSEVIER)S0888-7543(20)30505-X DE-627 ger DE-627 rda eng 610 570 DE-600 42.20 bkl 44.48 bkl Xiao, Qiu verfasserin aut An 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy. Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning Yu, Haiming verfasserin aut Zhong, Jiancheng verfasserin aut Liang, Cheng verfasserin aut Li, Guanghui verfasserin aut Ding, Pingjian verfasserin aut Luo, Jiawei verfasserin aut Enthalten in Genomics San Diego, Calif. : Academic Press, 1987 112, Seite 3407-3415 Online-Ressource (DE-627)266881815 (DE-600)1468023-3 (DE-576)078707269 1089-8646 nnns volume:112 pages:3407-3415 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 Genetik 44.48 Medizinische Genetik AR 112 3407-3415
allfieldsSound	10.1016/j.ygeno.2020.06.017 doi (DE-627)ELV004373006 (ELSEVIER)S0888-7543(20)30505-X DE-627 ger DE-627 rda eng 610 570 DE-600 42.20 bkl 44.48 bkl Xiao, Qiu verfasserin aut An 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy. Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning Yu, Haiming verfasserin aut Zhong, Jiancheng verfasserin aut Liang, Cheng verfasserin aut Li, Guanghui verfasserin aut Ding, Pingjian verfasserin aut Luo, Jiawei verfasserin aut Enthalten in Genomics San Diego, Calif. : Academic Press, 1987 112, Seite 3407-3415 Online-Ressource (DE-627)266881815 (DE-600)1468023-3 (DE-576)078707269 1089-8646 nnns volume:112 pages:3407-3415 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.20 Genetik 44.48 Medizinische Genetik AR 112 3407-3415
language	English
source	Enthalten in Genomics 112, Seite 3407-3415 volume:112 pages:3407-3415
sourceStr	Enthalten in Genomics 112, Seite 3407-3415 volume:112 pages:3407-3415
format_phy_str_mv	Article
bklname	Genetik Medizinische Genetik
institution	findex.gbv.de
topic_facet	Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning
dewey-raw	610
isfreeaccess_bool	false
container_title	Genomics
authorswithroles_txt_mv	Xiao, Qiu @@aut@@ Yu, Haiming @@aut@@ Zhong, Jiancheng @@aut@@ Liang, Cheng @@aut@@ Li, Guanghui @@aut@@ Ding, Pingjian @@aut@@ Luo, Jiawei @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	266881815
dewey-sort	3610
id	ELV004373006
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV004373006</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524161321.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ygeno.2020.06.017</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV004373006</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0888-7543(20)30505-X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="a">570</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xiao, Qiu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Circular RNA (circRNA)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disease circRNA prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">circRNA-disease network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-label learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Haiming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Jiancheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Guanghui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ding, Pingjian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Jiawei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Genomics</subfield><subfield code="d">San Diego, Calif. : Academic Press, 1987</subfield><subfield code="g">112, Seite 3407-3415</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)266881815</subfield><subfield code="w">(DE-600)1468023-3</subfield><subfield code="w">(DE-576)078707269</subfield><subfield code="x">1089-8646</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:112</subfield><subfield code="g">pages:3407-3415</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.20</subfield><subfield code="j">Genetik</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.48</subfield><subfield code="j">Medizinische Genetik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">112</subfield><subfield code="h">3407-3415</subfield></datafield></record></collection>
author	Xiao, Qiu
spellingShingle	Xiao, Qiu ddc 610 bkl 42.20 bkl 44.48 misc Circular RNA (circRNA) misc Disease circRNA prediction misc circRNA-disease network misc Multi-label learning An
authorStr	Xiao, Qiu
ppnlink_with_tag_str_mv	@@773@@(DE-627)266881815
format	electronic Article
dewey-ones	610 - Medicine & health 570 - Life sciences; biology
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	1089-8646
topic_title	610 570 DE-600 42.20 bkl 44.48 bkl An Circular RNA (circRNA) Disease circRNA prediction circRNA-disease network Multi-label learning
topic	ddc 610 bkl 42.20 bkl 44.48 misc Circular RNA (circRNA) misc Disease circRNA prediction misc circRNA-disease network misc Multi-label learning
topic_unstemmed	ddc 610 bkl 42.20 bkl 44.48 misc Circular RNA (circRNA) misc Disease circRNA prediction misc circRNA-disease network misc Multi-label learning
topic_browse	ddc 610 bkl 42.20 bkl 44.48 misc Circular RNA (circRNA) misc Disease circRNA prediction misc circRNA-disease network misc Multi-label learning
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Genomics
hierarchy_parent_id	266881815
dewey-tens	610 - Medicine & health 570 - Life sciences; biology
hierarchy_top_title	Genomics
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)266881815 (DE-600)1468023-3 (DE-576)078707269
title	An
ctrlnum	(DE-627)ELV004373006 (ELSEVIER)S0888-7543(20)30505-X
title_full	An
author_sort	Xiao, Qiu
journal	Genomics
journalStr	Genomics
lang_code	eng
isOA_bool	false
dewey-hundreds	600 - Technology 500 - Science
recordtype	marc
publishDateSort	2020
contenttype_str_mv	zzz
container_start_page	3407
author_browse	Xiao, Qiu Yu, Haiming Zhong, Jiancheng Liang, Cheng Li, Guanghui Ding, Pingjian Luo, Jiawei
container_volume	112
class	610 570 DE-600 42.20 bkl 44.48 bkl
format_se	Elektronische Aufsätze
author-letter	Xiao, Qiu
doi_str_mv	10.1016/j.ygeno.2020.06.017
dewey-full	610 570
author2-role	verfasserin
title_sort	an
title_auth	An
abstract	Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.
abstractGer	Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.
abstract_unstemmed	Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.
collection_details	GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 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_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	An
remote_bool	true
author2	Yu, Haiming Zhong, Jiancheng Liang, Cheng Li, Guanghui Ding, Pingjian Luo, Jiawei
author2Str	Yu, Haiming Zhong, Jiancheng Liang, Cheng Li, Guanghui Ding, Pingjian Luo, Jiawei
ppnlink	266881815
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.ygeno.2020.06.017
up_date	2024-07-06T22:46:15.813Z
_version_	1803871558896189440
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV004373006</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524161321.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ygeno.2020.06.017</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV004373006</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0888-7543(20)30505-X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="a">570</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xiao, Qiu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Circular RNA (circRNA)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disease circRNA prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">circRNA-disease network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-label learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Haiming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Jiancheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Guanghui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ding, Pingjian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Jiawei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Genomics</subfield><subfield code="d">San Diego, Calif. : Academic Press, 1987</subfield><subfield code="g">112, Seite 3407-3415</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)266881815</subfield><subfield code="w">(DE-600)1468023-3</subfield><subfield code="w">(DE-576)078707269</subfield><subfield code="x">1089-8646</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:112</subfield><subfield code="g">pages:3407-3415</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.20</subfield><subfield code="j">Genetik</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.48</subfield><subfield code="j">Medizinische Genetik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">112</subfield><subfield code="h">3407-3415</subfield></datafield></record></collection>
score	7.400467

Nicht das Richtige dabei?

Schreiben Sie uns!

An

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?