Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiment...
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Autor*in:	Liqian Zhou [verfasserIn] Xinhuai Peng [verfasserIn] Lijun Zeng [verfasserIn] Lihong Peng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network

Übergeordnetes Werk:	In: Frontiers in Genetics - Frontiers Media S.A., 2011, 15(2024)
Übergeordnetes Werk:	volume:15 ; year:2024

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fgene.2024.1356205

Katalog-ID:	DOAJ101462581

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520			\|a Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.
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allfields	10.3389/fgene.2024.1356205 doi (DE-627)DOAJ101462581 (DE-599)DOAJba0eeda1c9ae409ab9266a5fa34746fe DE-627 ger DE-627 rakwb eng QH426-470 Liqian Zhou verfasserin aut Finding potential lncRNA–disease associations using a boosting-based ensemble learning model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification. lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network Genetics Xinhuai Peng verfasserin aut Lijun Zeng verfasserin aut Lihong Peng verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 15(2024) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:15 year:2024 https://doi.org/10.3389/fgene.2024.1356205 kostenfrei https://doaj.org/article/ba0eeda1c9ae409ab9266a5fa34746fe kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2024.1356205/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 15 2024
spelling	10.3389/fgene.2024.1356205 doi (DE-627)DOAJ101462581 (DE-599)DOAJba0eeda1c9ae409ab9266a5fa34746fe DE-627 ger DE-627 rakwb eng QH426-470 Liqian Zhou verfasserin aut Finding potential lncRNA–disease associations using a boosting-based ensemble learning model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification. lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network Genetics Xinhuai Peng verfasserin aut Lijun Zeng verfasserin aut Lihong Peng verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 15(2024) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:15 year:2024 https://doi.org/10.3389/fgene.2024.1356205 kostenfrei https://doaj.org/article/ba0eeda1c9ae409ab9266a5fa34746fe kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2024.1356205/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 15 2024
allfields_unstemmed	10.3389/fgene.2024.1356205 doi (DE-627)DOAJ101462581 (DE-599)DOAJba0eeda1c9ae409ab9266a5fa34746fe DE-627 ger DE-627 rakwb eng QH426-470 Liqian Zhou verfasserin aut Finding potential lncRNA–disease associations using a boosting-based ensemble learning model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification. lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network Genetics Xinhuai Peng verfasserin aut Lijun Zeng verfasserin aut Lihong Peng verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 15(2024) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:15 year:2024 https://doi.org/10.3389/fgene.2024.1356205 kostenfrei https://doaj.org/article/ba0eeda1c9ae409ab9266a5fa34746fe kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2024.1356205/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 15 2024
allfieldsGer	10.3389/fgene.2024.1356205 doi (DE-627)DOAJ101462581 (DE-599)DOAJba0eeda1c9ae409ab9266a5fa34746fe DE-627 ger DE-627 rakwb eng QH426-470 Liqian Zhou verfasserin aut Finding potential lncRNA–disease associations using a boosting-based ensemble learning model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification. lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network Genetics Xinhuai Peng verfasserin aut Lijun Zeng verfasserin aut Lihong Peng verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 15(2024) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:15 year:2024 https://doi.org/10.3389/fgene.2024.1356205 kostenfrei https://doaj.org/article/ba0eeda1c9ae409ab9266a5fa34746fe kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2024.1356205/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 15 2024
allfieldsSound	10.3389/fgene.2024.1356205 doi (DE-627)DOAJ101462581 (DE-599)DOAJba0eeda1c9ae409ab9266a5fa34746fe DE-627 ger DE-627 rakwb eng QH426-470 Liqian Zhou verfasserin aut Finding potential lncRNA–disease associations using a boosting-based ensemble learning model 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification. lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network Genetics Xinhuai Peng verfasserin aut Lijun Zeng verfasserin aut Lihong Peng verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 15(2024) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:15 year:2024 https://doi.org/10.3389/fgene.2024.1356205 kostenfrei https://doaj.org/article/ba0eeda1c9ae409ab9266a5fa34746fe kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2024.1356205/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 15 2024
language	English
source	In Frontiers in Genetics 15(2024) volume:15 year:2024
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topic_facet	lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network Genetics
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container_title	Frontiers in Genetics
authorswithroles_txt_mv	Liqian Zhou @@aut@@ Xinhuai Peng @@aut@@ Lijun Zeng @@aut@@ Lihong Peng @@aut@@
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callnumber-first	Q - Science
author	Liqian Zhou
spellingShingle	Liqian Zhou misc QH426-470 misc lncRNA–disease association misc singular value decomposition misc LightGBM misc AdaBoost misc convolutional neural network misc Genetics Finding potential lncRNA–disease associations using a boosting-based ensemble learning model
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topic_title	QH426-470 Finding potential lncRNA–disease associations using a boosting-based ensemble learning model lncRNA–disease association singular value decomposition LightGBM AdaBoost convolutional neural network
topic	misc QH426-470 misc lncRNA–disease association misc singular value decomposition misc LightGBM misc AdaBoost misc convolutional neural network misc Genetics
topic_unstemmed	misc QH426-470 misc lncRNA–disease association misc singular value decomposition misc LightGBM misc AdaBoost misc convolutional neural network misc Genetics
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title	Finding potential lncRNA–disease associations using a boosting-based ensemble learning model
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title_full	Finding potential lncRNA–disease associations using a boosting-based ensemble learning model
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author_browse	Liqian Zhou Xinhuai Peng Lijun Zeng Lihong Peng
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title_sort	finding potential lncrna–disease associations using a boosting-based ensemble learning model
callnumber	QH426-470
title_auth	Finding potential lncRNA–disease associations using a boosting-based ensemble learning model
abstract	Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.
abstractGer	Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.
abstract_unstemmed	Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.
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title_short	Finding potential lncRNA–disease associations using a boosting-based ensemble learning model
url	https://doi.org/10.3389/fgene.2024.1356205 https://doaj.org/article/ba0eeda1c9ae409ab9266a5fa34746fe https://www.frontiersin.org/articles/10.3389/fgene.2024.1356205/full https://doaj.org/toc/1664-8021
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up_date	2024-07-03T20:46:31.026Z
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