Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network

Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have be...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Deng, Lei [verfasserIn] Wang, Junqiang Xiao, Yun Wang, Zixiang Liu, Hui

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Protein-lncRNA interaction Heterogenous network HeteSim score Gradient tree boosting

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 19(2018), 1 vom: 11. Okt.
Übergeordnetes Werk:	volume:19 ; year:2018 ; number:1 ; day:11 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s12859-018-2390-0

Katalog-ID:	SPR026918382

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520			\|a Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/.
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matchkey_str	article:14712105:2018----::cuaerdcinfrtilcnitrcinbdfuinnhtsmetrs
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allfields	10.1186/s12859-018-2390-0 doi (DE-627)SPR026918382 (SPR)s12859-018-2390-0-e DE-627 ger DE-627 rakwb eng Deng, Lei verfasserin aut Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. Protein-lncRNA interaction (dpeaa)DE-He213 Heterogenous network (dpeaa)DE-He213 HeteSim score (dpeaa)DE-He213 Gradient tree boosting (dpeaa)DE-He213 Wang, Junqiang aut Xiao, Yun aut Wang, Zixiang aut Liu, Hui (orcid)0000-0001-7158-913X aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), 1 vom: 11. Okt. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:1 day:11 month:10 https://dx.doi.org/10.1186/s12859-018-2390-0 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 11 10
spelling	10.1186/s12859-018-2390-0 doi (DE-627)SPR026918382 (SPR)s12859-018-2390-0-e DE-627 ger DE-627 rakwb eng Deng, Lei verfasserin aut Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. Protein-lncRNA interaction (dpeaa)DE-He213 Heterogenous network (dpeaa)DE-He213 HeteSim score (dpeaa)DE-He213 Gradient tree boosting (dpeaa)DE-He213 Wang, Junqiang aut Xiao, Yun aut Wang, Zixiang aut Liu, Hui (orcid)0000-0001-7158-913X aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), 1 vom: 11. Okt. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:1 day:11 month:10 https://dx.doi.org/10.1186/s12859-018-2390-0 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 11 10
allfields_unstemmed	10.1186/s12859-018-2390-0 doi (DE-627)SPR026918382 (SPR)s12859-018-2390-0-e DE-627 ger DE-627 rakwb eng Deng, Lei verfasserin aut Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. Protein-lncRNA interaction (dpeaa)DE-He213 Heterogenous network (dpeaa)DE-He213 HeteSim score (dpeaa)DE-He213 Gradient tree boosting (dpeaa)DE-He213 Wang, Junqiang aut Xiao, Yun aut Wang, Zixiang aut Liu, Hui (orcid)0000-0001-7158-913X aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), 1 vom: 11. Okt. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:1 day:11 month:10 https://dx.doi.org/10.1186/s12859-018-2390-0 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 11 10
allfieldsGer	10.1186/s12859-018-2390-0 doi (DE-627)SPR026918382 (SPR)s12859-018-2390-0-e DE-627 ger DE-627 rakwb eng Deng, Lei verfasserin aut Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. Protein-lncRNA interaction (dpeaa)DE-He213 Heterogenous network (dpeaa)DE-He213 HeteSim score (dpeaa)DE-He213 Gradient tree boosting (dpeaa)DE-He213 Wang, Junqiang aut Xiao, Yun aut Wang, Zixiang aut Liu, Hui (orcid)0000-0001-7158-913X aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), 1 vom: 11. Okt. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:1 day:11 month:10 https://dx.doi.org/10.1186/s12859-018-2390-0 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 11 10
allfieldsSound	10.1186/s12859-018-2390-0 doi (DE-627)SPR026918382 (SPR)s12859-018-2390-0-e DE-627 ger DE-627 rakwb eng Deng, Lei verfasserin aut Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. Protein-lncRNA interaction (dpeaa)DE-He213 Heterogenous network (dpeaa)DE-He213 HeteSim score (dpeaa)DE-He213 Gradient tree boosting (dpeaa)DE-He213 Wang, Junqiang aut Xiao, Yun aut Wang, Zixiang aut Liu, Hui (orcid)0000-0001-7158-913X aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 19(2018), 1 vom: 11. Okt. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:19 year:2018 number:1 day:11 month:10 https://dx.doi.org/10.1186/s12859-018-2390-0 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 11 10
language	English
source	Enthalten in BMC bioinformatics 19(2018), 1 vom: 11. Okt. volume:19 year:2018 number:1 day:11 month:10
sourceStr	Enthalten in BMC bioinformatics 19(2018), 1 vom: 11. Okt. volume:19 year:2018 number:1 day:11 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Protein-lncRNA interaction Heterogenous network HeteSim score Gradient tree boosting
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Deng, Lei @@aut@@ Wang, Junqiang @@aut@@ Xiao, Yun @@aut@@ Wang, Zixiang @@aut@@ Liu, Hui @@aut@@
publishDateDaySort_date	2018-10-11T00:00:00Z
hierarchy_top_id	326644814
id	SPR026918382
language_de	englisch
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author	Deng, Lei
spellingShingle	Deng, Lei misc Protein-lncRNA interaction misc Heterogenous network misc HeteSim score misc Gradient tree boosting Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network
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topic_title	Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network Protein-lncRNA interaction (dpeaa)DE-He213 Heterogenous network (dpeaa)DE-He213 HeteSim score (dpeaa)DE-He213 Gradient tree boosting (dpeaa)DE-He213
topic	misc Protein-lncRNA interaction misc Heterogenous network misc HeteSim score misc Gradient tree boosting
topic_unstemmed	misc Protein-lncRNA interaction misc Heterogenous network misc HeteSim score misc Gradient tree boosting
topic_browse	misc Protein-lncRNA interaction misc Heterogenous network misc HeteSim score misc Gradient tree boosting
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title	Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network
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title_full	Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network
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author_browse	Deng, Lei Wang, Junqiang Xiao, Yun Wang, Zixiang Liu, Hui
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title_sort	accurate prediction of protein-lncrna interactions by diffusion and hetesim features across heterogeneous network
title_auth	Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network
abstract	Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. © The Author(s) 2018
abstractGer	Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. © The Author(s) 2018
abstract_unstemmed	Background Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. Results In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. Availability The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/. © The Author(s) 2018
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title_short	Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network
url	https://dx.doi.org/10.1186/s12859-018-2390-0
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author2	Wang, Junqiang Xiao, Yun Wang, Zixiang Liu, Hui
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up_date	2024-07-03T23:26:44.554Z
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Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network

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