Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lihong Peng [verfasserIn] Liangliang Huang [verfasserIn] Geng Tian [verfasserIn] Yan Wu [verfasserIn] Guang Li [verfasserIn] Jianying Cao [verfasserIn] Peng Wang [verfasserIn] Zejun Li [verfasserIn] Lian Duan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network

Übergeordnetes Werk:	In: Frontiers in Microbiology - Frontiers Media S.A., 2011, 14(2023)
Übergeordnetes Werk:	volume:14 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fmicb.2023.1244527

Katalog-ID:	DOAJ100245803

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520			\|a BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.
650		4	\|a microbe-disease associations
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700	0		\|a Geng Tian \|e verfasserin \|4 aut
700	0		\|a Yan Wu \|e verfasserin \|4 aut
700	0		\|a Guang Li \|e verfasserin \|4 aut
700	0		\|a Guang Li \|e verfasserin \|4 aut
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allfields	10.3389/fmicb.2023.1244527 doi (DE-627)DOAJ100245803 (DE-599)DOAJfc5c41f245a24481a1c32de8dd44545f DE-627 ger DE-627 rakwb eng QR1-502 Lihong Peng verfasserin aut Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network Microbiology Lihong Peng verfasserin aut Liangliang Huang verfasserin aut Geng Tian verfasserin aut Yan Wu verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Peng Wang verfasserin aut Zejun Li verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut In Frontiers in Microbiology Frontiers Media S.A., 2011 14(2023) (DE-627)642889384 (DE-600)2587354-4 1664302X nnns volume:14 year:2023 https://doi.org/10.3389/fmicb.2023.1244527 kostenfrei https://doaj.org/article/fc5c41f245a24481a1c32de8dd44545f kostenfrei https://www.frontiersin.org/articles/10.3389/fmicb.2023.1244527/full kostenfrei https://doaj.org/toc/1664-302X 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 14 2023
spelling	10.3389/fmicb.2023.1244527 doi (DE-627)DOAJ100245803 (DE-599)DOAJfc5c41f245a24481a1c32de8dd44545f DE-627 ger DE-627 rakwb eng QR1-502 Lihong Peng verfasserin aut Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network Microbiology Lihong Peng verfasserin aut Liangliang Huang verfasserin aut Geng Tian verfasserin aut Yan Wu verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Peng Wang verfasserin aut Zejun Li verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut In Frontiers in Microbiology Frontiers Media S.A., 2011 14(2023) (DE-627)642889384 (DE-600)2587354-4 1664302X nnns volume:14 year:2023 https://doi.org/10.3389/fmicb.2023.1244527 kostenfrei https://doaj.org/article/fc5c41f245a24481a1c32de8dd44545f kostenfrei https://www.frontiersin.org/articles/10.3389/fmicb.2023.1244527/full kostenfrei https://doaj.org/toc/1664-302X 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 14 2023
allfields_unstemmed	10.3389/fmicb.2023.1244527 doi (DE-627)DOAJ100245803 (DE-599)DOAJfc5c41f245a24481a1c32de8dd44545f DE-627 ger DE-627 rakwb eng QR1-502 Lihong Peng verfasserin aut Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network Microbiology Lihong Peng verfasserin aut Liangliang Huang verfasserin aut Geng Tian verfasserin aut Yan Wu verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Peng Wang verfasserin aut Zejun Li verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut In Frontiers in Microbiology Frontiers Media S.A., 2011 14(2023) (DE-627)642889384 (DE-600)2587354-4 1664302X nnns volume:14 year:2023 https://doi.org/10.3389/fmicb.2023.1244527 kostenfrei https://doaj.org/article/fc5c41f245a24481a1c32de8dd44545f kostenfrei https://www.frontiersin.org/articles/10.3389/fmicb.2023.1244527/full kostenfrei https://doaj.org/toc/1664-302X 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 14 2023
allfieldsGer	10.3389/fmicb.2023.1244527 doi (DE-627)DOAJ100245803 (DE-599)DOAJfc5c41f245a24481a1c32de8dd44545f DE-627 ger DE-627 rakwb eng QR1-502 Lihong Peng verfasserin aut Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network Microbiology Lihong Peng verfasserin aut Liangliang Huang verfasserin aut Geng Tian verfasserin aut Yan Wu verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Peng Wang verfasserin aut Zejun Li verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut In Frontiers in Microbiology Frontiers Media S.A., 2011 14(2023) (DE-627)642889384 (DE-600)2587354-4 1664302X nnns volume:14 year:2023 https://doi.org/10.3389/fmicb.2023.1244527 kostenfrei https://doaj.org/article/fc5c41f245a24481a1c32de8dd44545f kostenfrei https://www.frontiersin.org/articles/10.3389/fmicb.2023.1244527/full kostenfrei https://doaj.org/toc/1664-302X 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 14 2023
allfieldsSound	10.3389/fmicb.2023.1244527 doi (DE-627)DOAJ100245803 (DE-599)DOAJfc5c41f245a24481a1c32de8dd44545f DE-627 ger DE-627 rakwb eng QR1-502 Lihong Peng verfasserin aut Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases. microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network Microbiology Lihong Peng verfasserin aut Liangliang Huang verfasserin aut Geng Tian verfasserin aut Yan Wu verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Guang Li verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Jianying Cao verfasserin aut Peng Wang verfasserin aut Zejun Li verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut Lian Duan verfasserin aut In Frontiers in Microbiology Frontiers Media S.A., 2011 14(2023) (DE-627)642889384 (DE-600)2587354-4 1664302X nnns volume:14 year:2023 https://doi.org/10.3389/fmicb.2023.1244527 kostenfrei https://doaj.org/article/fc5c41f245a24481a1c32de8dd44545f kostenfrei https://www.frontiersin.org/articles/10.3389/fmicb.2023.1244527/full kostenfrei https://doaj.org/toc/1664-302X 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 14 2023
language	English
source	In Frontiers in Microbiology 14(2023) volume:14 year:2023
sourceStr	In Frontiers in Microbiology 14(2023) volume:14 year:2023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network Microbiology
isfreeaccess_bool	true
container_title	Frontiers in Microbiology
authorswithroles_txt_mv	Lihong Peng @@aut@@ Liangliang Huang @@aut@@ Geng Tian @@aut@@ Yan Wu @@aut@@ Guang Li @@aut@@ Jianying Cao @@aut@@ Peng Wang @@aut@@ Zejun Li @@aut@@ Lian Duan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	642889384
id	DOAJ100245803
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ100245803</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414085130.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240414s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fmicb.2023.1244527</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ100245803</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJfc5c41f245a24481a1c32de8dd44545f</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">QR1-502</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Lihong Peng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">microbe-disease associations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">graph attention autoencoder</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">positive-unlabeled learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-means</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">XGBoost</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep neural network</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Microbiology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lihong Peng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" 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callnumber-first	Q - Science
author	Lihong Peng
spellingShingle	Lihong Peng misc QR1-502 misc microbe-disease associations misc graph attention autoencoder misc positive-unlabeled learning misc K-means misc XGBoost misc deep neural network misc Microbiology Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
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topic_title	QR1-502 Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network microbe-disease associations graph attention autoencoder positive-unlabeled learning K-means XGBoost deep neural network
topic	misc QR1-502 misc microbe-disease associations misc graph attention autoencoder misc positive-unlabeled learning misc K-means misc XGBoost misc deep neural network misc Microbiology
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title	Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
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title_full	Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
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author_browse	Lihong Peng Liangliang Huang Geng Tian Yan Wu Guang Li Jianying Cao Peng Wang Zejun Li Lian Duan
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title_sort	predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
callnumber	QR1-502
title_auth	Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
abstract	BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.
abstractGer	BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.
abstract_unstemmed	BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.
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title_short	Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network
url	https://doi.org/10.3389/fmicb.2023.1244527 https://doaj.org/article/fc5c41f245a24481a1c32de8dd44545f https://www.frontiersin.org/articles/10.3389/fmicb.2023.1244527/full https://doaj.org/toc/1664-302X
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author2	Lihong Peng Liangliang Huang Geng Tian Yan Wu Guang Li Jianying Cao Peng Wang Zejun Li Lian Duan
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Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. 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Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

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