A review on compound-protein interaction prediction methods: Data, format, representation and model

There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been ac...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sangsoo Lim [verfasserIn] Yijingxiu Lu [verfasserIn] Chang Yun Cho [verfasserIn] Inyoung Sung [verfasserIn] Jungwoo Kim [verfasserIn] Youngkuk Kim [verfasserIn] Sungjoon Park [verfasserIn] Sun Kim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning

Übergeordnetes Werk:	In: Computational and Structural Biotechnology Journal - Elsevier, 2013, 19(2021), Seite 1541-1556
Übergeordnetes Werk:	volume:19 ; year:2021 ; pages:1541-1556

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.csbj.2021.03.004

Katalog-ID:	DOAJ019082924

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ019082924
003	DE-627
005	20230503094716.0
007	cr uuu---uuuuu
008	230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.csbj.2021.03.004 \|2 doi
035			\|a (DE-627)DOAJ019082924
035			\|a (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TP248.13-248.65
100	0		\|a Sangsoo Lim \|e verfasserin \|4 aut
245	1	2	\|a A review on compound-protein interaction prediction methods: Data, format, representation and model
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
650		4	\|a Compound-protein interaction
650		4	\|a Data representation
650		4	\|a Interpretable learning
650		4	\|a Chemical descriptors
650		4	\|a Protein descriptors
650		4	\|a Machine learning
653		0	\|a Biotechnology
700	0		\|a Yijingxiu Lu \|e verfasserin \|4 aut
700	0		\|a Chang Yun Cho \|e verfasserin \|4 aut
700	0		\|a Inyoung Sung \|e verfasserin \|4 aut
700	0		\|a Jungwoo Kim \|e verfasserin \|4 aut
700	0		\|a Youngkuk Kim \|e verfasserin \|4 aut
700	0		\|a Sungjoon Park \|e verfasserin \|4 aut
700	0		\|a Sun Kim \|e verfasserin \|4 aut
773	0	8	\|i In \|t Computational and Structural Biotechnology Journal \|d Elsevier, 2013 \|g 19(2021), Seite 1541-1556 \|w (DE-627)731890086 \|w (DE-600)2694435-2 \|x 20010370 \|7 nnns
773	1	8	\|g volume:19 \|g year:2021 \|g pages:1541-1556
856	4	0	\|u https://doi.org/10.1016/j.csbj.2021.03.004 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f \|z kostenfrei
856	4	0	\|u http://www.sciencedirect.com/science/article/pii/S2001037021000763 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2001-0370 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
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_95
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_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
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_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 19 \|j 2021 \|h 1541-1556

Indexfelder

author_variant	s l sl y l yl c y c cyc i s is j k jk y k yk s p sp s k sk
matchkey_str	article:20010370:2021----::rveocmonpoenneatopeitomtosaaomt
hierarchy_sort_str	2021
callnumber-subject-code	TP
publishDate	2021
allfields	10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 19 2021 1541-1556
spelling	10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 19 2021 1541-1556
allfields_unstemmed	10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 19 2021 1541-1556
allfieldsGer	10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 19 2021 1541-1556
allfieldsSound	10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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 19 2021 1541-1556
language	English
source	In Computational and Structural Biotechnology Journal 19(2021), Seite 1541-1556 volume:19 year:2021 pages:1541-1556
sourceStr	In Computational and Structural Biotechnology Journal 19(2021), Seite 1541-1556 volume:19 year:2021 pages:1541-1556
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology
isfreeaccess_bool	true
container_title	Computational and Structural Biotechnology Journal
authorswithroles_txt_mv	Sangsoo Lim @@aut@@ Yijingxiu Lu @@aut@@ Chang Yun Cho @@aut@@ Inyoung Sung @@aut@@ Jungwoo Kim @@aut@@ Youngkuk Kim @@aut@@ Sungjoon Park @@aut@@ Sun Kim @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	731890086
id	DOAJ019082924
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">DOAJ019082924</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503094716.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.csbj.2021.03.004</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ019082924</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJc02a7507566b4e85af7cec5e7835789f</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP248.13-248.65</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sangsoo Lim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A review on compound-protein interaction prediction methods: Data, format, representation and model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Compound-protein interaction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data representation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Interpretable learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chemical descriptors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Protein descriptors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biotechnology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yijingxiu Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chang Yun Cho</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Inyoung Sung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jungwoo Kim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Youngkuk Kim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sungjoon Park</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sun Kim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Computational and Structural Biotechnology Journal</subfield><subfield code="d">Elsevier, 2013</subfield><subfield code="g">19(2021), Seite 1541-1556</subfield><subfield code="w">(DE-627)731890086</subfield><subfield code="w">(DE-600)2694435-2</subfield><subfield code="x">20010370</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:19</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:1541-1556</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.csbj.2021.03.004</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S2001037021000763</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2001-0370</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</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_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_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_95</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_213</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_602</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_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_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_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_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="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">19</subfield><subfield code="j">2021</subfield><subfield code="h">1541-1556</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Sangsoo Lim
spellingShingle	Sangsoo Lim misc TP248.13-248.65 misc Compound-protein interaction misc Data representation misc Interpretable learning misc Chemical descriptors misc Protein descriptors misc Machine learning misc Biotechnology A review on compound-protein interaction prediction methods: Data, format, representation and model
authorStr	Sangsoo Lim
ppnlink_with_tag_str_mv	@@773@@(DE-627)731890086
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TP248
illustrated	Not Illustrated
issn	20010370
topic_title	TP248.13-248.65 A review on compound-protein interaction prediction methods: Data, format, representation and model Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning
topic	misc TP248.13-248.65 misc Compound-protein interaction misc Data representation misc Interpretable learning misc Chemical descriptors misc Protein descriptors misc Machine learning misc Biotechnology
topic_unstemmed	misc TP248.13-248.65 misc Compound-protein interaction misc Data representation misc Interpretable learning misc Chemical descriptors misc Protein descriptors misc Machine learning misc Biotechnology
topic_browse	misc TP248.13-248.65 misc Compound-protein interaction misc Data representation misc Interpretable learning misc Chemical descriptors misc Protein descriptors misc Machine learning misc Biotechnology
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Computational and Structural Biotechnology Journal
hierarchy_parent_id	731890086
hierarchy_top_title	Computational and Structural Biotechnology Journal
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)731890086 (DE-600)2694435-2
title	A review on compound-protein interaction prediction methods: Data, format, representation and model
ctrlnum	(DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f
title_full	A review on compound-protein interaction prediction methods: Data, format, representation and model
author_sort	Sangsoo Lim
journal	Computational and Structural Biotechnology Journal
journalStr	Computational and Structural Biotechnology Journal
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
container_start_page	1541
author_browse	Sangsoo Lim Yijingxiu Lu Chang Yun Cho Inyoung Sung Jungwoo Kim Youngkuk Kim Sungjoon Park Sun Kim
container_volume	19
class	TP248.13-248.65
format_se	Elektronische Aufsätze
author-letter	Sangsoo Lim
doi_str_mv	10.1016/j.csbj.2021.03.004
author2-role	verfasserin
title_sort	review on compound-protein interaction prediction methods: data, format, representation and model
callnumber	TP248.13-248.65
title_auth	A review on compound-protein interaction prediction methods: Data, format, representation and model
abstract	There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
abstractGer	There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
abstract_unstemmed	There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 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
title_short	A review on compound-protein interaction prediction methods: Data, format, representation and model
url	https://doi.org/10.1016/j.csbj.2021.03.004 https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f http://www.sciencedirect.com/science/article/pii/S2001037021000763 https://doaj.org/toc/2001-0370
remote_bool	true
author2	Yijingxiu Lu Chang Yun Cho Inyoung Sung Jungwoo Kim Youngkuk Kim Sungjoon Park Sun Kim
author2Str	Yijingxiu Lu Chang Yun Cho Inyoung Sung Jungwoo Kim Youngkuk Kim Sungjoon Park Sun Kim
ppnlink	731890086
callnumber-subject	TP - Chemical Technology
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1016/j.csbj.2021.03.004
callnumber-a	TP248.13-248.65
up_date	2024-07-03T21:41:26.919Z
_version_	1803595690195025920
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">DOAJ019082924</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503094716.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.csbj.2021.03.004</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ019082924</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJc02a7507566b4e85af7cec5e7835789f</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP248.13-248.65</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sangsoo Lim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A review on compound-protein interaction prediction methods: Data, format, representation and model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Compound-protein interaction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data representation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Interpretable learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chemical descriptors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Protein descriptors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biotechnology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yijingxiu Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chang Yun Cho</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Inyoung Sung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jungwoo Kim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Youngkuk Kim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sungjoon Park</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sun Kim</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Computational and Structural Biotechnology Journal</subfield><subfield code="d">Elsevier, 2013</subfield><subfield code="g">19(2021), Seite 1541-1556</subfield><subfield code="w">(DE-627)731890086</subfield><subfield code="w">(DE-600)2694435-2</subfield><subfield code="x">20010370</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:19</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:1541-1556</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.csbj.2021.03.004</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S2001037021000763</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2001-0370</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</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_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_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_95</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_213</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_602</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_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_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_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_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="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">19</subfield><subfield code="j">2021</subfield><subfield code="h">1541-1556</subfield></datafield></record></collection>
score	7.4005537

Nicht das Richtige dabei?

Schreiben Sie uns!

A review on compound-protein interaction prediction methods: Data, format, representation and model

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?