Prediction of protein structural classes using hybrid properties

Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Wenjin [verfasserIn] Lin, Kao [verfasserIn] Feng, Kaiyan [verfasserIn] Cai, Yudong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Protein structural class Nearest neighbor algorithm mRMR (Minimum Redundancy, Maximum Relevance) Physiochemical properties Amino acid compositions

Übergeordnetes Werk:	Enthalten in: Molecular diversity - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1995, 12(2008), 3-4 vom: Aug., Seite 171-179
Übergeordnetes Werk:	volume:12 ; year:2008 ; number:3-4 ; month:08 ; pages:171-179

Links:	Volltext

DOI / URN:	10.1007/s11030-008-9093-9

Katalog-ID:	SPR015827453

Internformat


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245	1	0	\|a Prediction of protein structural classes using hybrid properties
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520			\|a Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp.
650		4	\|a Protein structural class \|7 (dpeaa)DE-He213
650		4	\|a Nearest neighbor algorithm \|7 (dpeaa)DE-He213
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650		4	\|a Physiochemical properties \|7 (dpeaa)DE-He213
650		4	\|a Amino acid compositions \|7 (dpeaa)DE-He213
700	1		\|a Lin, Kao \|e verfasserin \|4 aut
700	1		\|a Feng, Kaiyan \|e verfasserin \|4 aut
700	1		\|a Cai, Yudong \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Molecular diversity \|d Dordrecht [u.a.] : Springer Science + Business Media B.V., 1995 \|g 12(2008), 3-4 vom: Aug., Seite 171-179 \|w (DE-627)311010377 \|w (DE-600)2003589-5 \|x 1573-501X \|7 nnns
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912			\|a GBV_USEFLAG_A
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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_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_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
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_4242
912			\|a GBV_ILN_4246
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_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 42.00 \|q ASE
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952			\|d 12 \|j 2008 \|e 3-4 \|c 08 \|h 171-179

Indexfelder

author_variant	w l wl k l kl k f kf y c yc
matchkey_str	article:1573501X:2008----::rdcinfrtisrcuacasssnh
hierarchy_sort_str	2008
bklnumber	42.00
publishDate	2008
allfields	10.1007/s11030-008-9093-9 doi (DE-627)SPR015827453 (SPR)s11030-008-9093-9-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Li, Wenjin verfasserin aut Prediction of protein structural classes using hybrid properties 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp. Protein structural class (dpeaa)DE-He213 Nearest neighbor algorithm (dpeaa)DE-He213 mRMR (Minimum Redundancy, Maximum Relevance) (dpeaa)DE-He213 Physiochemical properties (dpeaa)DE-He213 Amino acid compositions (dpeaa)DE-He213 Lin, Kao verfasserin aut Feng, Kaiyan verfasserin aut Cai, Yudong verfasserin aut Enthalten in Molecular diversity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1995 12(2008), 3-4 vom: Aug., Seite 171-179 (DE-627)311010377 (DE-600)2003589-5 1573-501X nnns volume:12 year:2008 number:3-4 month:08 pages:171-179 https://dx.doi.org/10.1007/s11030-008-9093-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 12 2008 3-4 08 171-179
spelling	10.1007/s11030-008-9093-9 doi (DE-627)SPR015827453 (SPR)s11030-008-9093-9-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Li, Wenjin verfasserin aut Prediction of protein structural classes using hybrid properties 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp. Protein structural class (dpeaa)DE-He213 Nearest neighbor algorithm (dpeaa)DE-He213 mRMR (Minimum Redundancy, Maximum Relevance) (dpeaa)DE-He213 Physiochemical properties (dpeaa)DE-He213 Amino acid compositions (dpeaa)DE-He213 Lin, Kao verfasserin aut Feng, Kaiyan verfasserin aut Cai, Yudong verfasserin aut Enthalten in Molecular diversity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1995 12(2008), 3-4 vom: Aug., Seite 171-179 (DE-627)311010377 (DE-600)2003589-5 1573-501X nnns volume:12 year:2008 number:3-4 month:08 pages:171-179 https://dx.doi.org/10.1007/s11030-008-9093-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 12 2008 3-4 08 171-179
allfields_unstemmed	10.1007/s11030-008-9093-9 doi (DE-627)SPR015827453 (SPR)s11030-008-9093-9-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Li, Wenjin verfasserin aut Prediction of protein structural classes using hybrid properties 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp. Protein structural class (dpeaa)DE-He213 Nearest neighbor algorithm (dpeaa)DE-He213 mRMR (Minimum Redundancy, Maximum Relevance) (dpeaa)DE-He213 Physiochemical properties (dpeaa)DE-He213 Amino acid compositions (dpeaa)DE-He213 Lin, Kao verfasserin aut Feng, Kaiyan verfasserin aut Cai, Yudong verfasserin aut Enthalten in Molecular diversity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1995 12(2008), 3-4 vom: Aug., Seite 171-179 (DE-627)311010377 (DE-600)2003589-5 1573-501X nnns volume:12 year:2008 number:3-4 month:08 pages:171-179 https://dx.doi.org/10.1007/s11030-008-9093-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 12 2008 3-4 08 171-179
allfieldsGer	10.1007/s11030-008-9093-9 doi (DE-627)SPR015827453 (SPR)s11030-008-9093-9-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Li, Wenjin verfasserin aut Prediction of protein structural classes using hybrid properties 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp. Protein structural class (dpeaa)DE-He213 Nearest neighbor algorithm (dpeaa)DE-He213 mRMR (Minimum Redundancy, Maximum Relevance) (dpeaa)DE-He213 Physiochemical properties (dpeaa)DE-He213 Amino acid compositions (dpeaa)DE-He213 Lin, Kao verfasserin aut Feng, Kaiyan verfasserin aut Cai, Yudong verfasserin aut Enthalten in Molecular diversity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1995 12(2008), 3-4 vom: Aug., Seite 171-179 (DE-627)311010377 (DE-600)2003589-5 1573-501X nnns volume:12 year:2008 number:3-4 month:08 pages:171-179 https://dx.doi.org/10.1007/s11030-008-9093-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 12 2008 3-4 08 171-179
allfieldsSound	10.1007/s11030-008-9093-9 doi (DE-627)SPR015827453 (SPR)s11030-008-9093-9-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Li, Wenjin verfasserin aut Prediction of protein structural classes using hybrid properties 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp. Protein structural class (dpeaa)DE-He213 Nearest neighbor algorithm (dpeaa)DE-He213 mRMR (Minimum Redundancy, Maximum Relevance) (dpeaa)DE-He213 Physiochemical properties (dpeaa)DE-He213 Amino acid compositions (dpeaa)DE-He213 Lin, Kao verfasserin aut Feng, Kaiyan verfasserin aut Cai, Yudong verfasserin aut Enthalten in Molecular diversity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1995 12(2008), 3-4 vom: Aug., Seite 171-179 (DE-627)311010377 (DE-600)2003589-5 1573-501X nnns volume:12 year:2008 number:3-4 month:08 pages:171-179 https://dx.doi.org/10.1007/s11030-008-9093-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 12 2008 3-4 08 171-179
language	English
source	Enthalten in Molecular diversity 12(2008), 3-4 vom: Aug., Seite 171-179 volume:12 year:2008 number:3-4 month:08 pages:171-179
sourceStr	Enthalten in Molecular diversity 12(2008), 3-4 vom: Aug., Seite 171-179 volume:12 year:2008 number:3-4 month:08 pages:171-179
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Protein structural class Nearest neighbor algorithm mRMR (Minimum Redundancy, Maximum Relevance) Physiochemical properties Amino acid compositions
dewey-raw	570
isfreeaccess_bool	false
container_title	Molecular diversity
authorswithroles_txt_mv	Li, Wenjin @@aut@@ Lin, Kao @@aut@@ Feng, Kaiyan @@aut@@ Cai, Yudong @@aut@@
publishDateDaySort_date	2008-08-01T00:00:00Z
hierarchy_top_id	311010377
dewey-sort	3570
id	SPR015827453
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">SPR015827453</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519100548.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2008 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11030-008-9093-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR015827453</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11030-008-9093-9-e</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="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Wenjin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of protein structural classes using hybrid properties</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</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">Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. 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author	Li, Wenjin
spellingShingle	Li, Wenjin ddc 570 bkl 42.00 misc Protein structural class misc Nearest neighbor algorithm misc mRMR (Minimum Redundancy, Maximum Relevance) misc Physiochemical properties misc Amino acid compositions Prediction of protein structural classes using hybrid properties
authorStr	Li, Wenjin
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topic_title	570 ASE 42.00 bkl Prediction of protein structural classes using hybrid properties Protein structural class (dpeaa)DE-He213 Nearest neighbor algorithm (dpeaa)DE-He213 mRMR (Minimum Redundancy, Maximum Relevance) (dpeaa)DE-He213 Physiochemical properties (dpeaa)DE-He213 Amino acid compositions (dpeaa)DE-He213
topic	ddc 570 bkl 42.00 misc Protein structural class misc Nearest neighbor algorithm misc mRMR (Minimum Redundancy, Maximum Relevance) misc Physiochemical properties misc Amino acid compositions
topic_unstemmed	ddc 570 bkl 42.00 misc Protein structural class misc Nearest neighbor algorithm misc mRMR (Minimum Redundancy, Maximum Relevance) misc Physiochemical properties misc Amino acid compositions
topic_browse	ddc 570 bkl 42.00 misc Protein structural class misc Nearest neighbor algorithm misc mRMR (Minimum Redundancy, Maximum Relevance) misc Physiochemical properties misc Amino acid compositions
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title	Prediction of protein structural classes using hybrid properties
ctrlnum	(DE-627)SPR015827453 (SPR)s11030-008-9093-9-e
title_full	Prediction of protein structural classes using hybrid properties
author_sort	Li, Wenjin
journal	Molecular diversity
journalStr	Molecular diversity
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author_browse	Li, Wenjin Lin, Kao Feng, Kaiyan Cai, Yudong
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title_sort	prediction of protein structural classes using hybrid properties
title_auth	Prediction of protein structural classes using hybrid properties
abstract	Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp.
abstractGer	Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp.
abstract_unstemmed	Abstract In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp.
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container_issue	3-4
title_short	Prediction of protein structural classes using hybrid properties
url	https://dx.doi.org/10.1007/s11030-008-9093-9
remote_bool	true
author2	Lin, Kao Feng, Kaiyan Cai, Yudong
author2Str	Lin, Kao Feng, Kaiyan Cai, Yudong
ppnlink	311010377
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11030-008-9093-9
up_date	2024-07-03T18:50:06.790Z
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Prediction of protein structural classes using hybrid properties

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