GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network
Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based o...
Saved in:
| Published in: | Frontiers in genetics Vol. 12; p. 650821 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Switzerland
Frontiers Media S.A
09.04.2021
|
| Subjects: | |
| ISSN: | 1664-8021, 1664-8021 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision–recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI. |
|---|---|
| AbstractList | Identifying drug-target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision-recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI.Identifying drug-target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision-recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI. Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision–recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI. |
| Author | Pan, Haiming Lan, Wei Pan, Shirui Liu, Zhixian Hao, Xinkun Chen, Qingfeng |
| AuthorAffiliation | 4 Department of Data Science and AI, Monash University , Melbourne, VIC , Australia 1 School of Medical, Guangxi University , Nanning , China 2 School of Electronics and Information Engineering, Beibu Gulf University , Qinzhou , China 3 School of Computer, Electronic and Information, Guangxi University , Nanning , China |
| AuthorAffiliation_xml | – name: 2 School of Electronics and Information Engineering, Beibu Gulf University , Qinzhou , China – name: 3 School of Computer, Electronic and Information, Guangxi University , Nanning , China – name: 4 Department of Data Science and AI, Monash University , Melbourne, VIC , Australia – name: 1 School of Medical, Guangxi University , Nanning , China |
| Author_xml | – sequence: 1 givenname: Zhixian surname: Liu fullname: Liu, Zhixian – sequence: 2 givenname: Qingfeng surname: Chen fullname: Chen, Qingfeng – sequence: 3 givenname: Wei surname: Lan fullname: Lan, Wei – sequence: 4 givenname: Haiming surname: Pan fullname: Pan, Haiming – sequence: 5 givenname: Xinkun surname: Hao fullname: Hao, Xinkun – sequence: 6 givenname: Shirui surname: Pan fullname: Pan, Shirui |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33912218$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kU1v1DAQhi1URD_oD-CCfOSyi7-S2ByQVoVuV6ooh3K2JvZkNyUbB8dbxL-vs9uiFglfbI1nnndm3lNy1IceCXnH2VxKbT42a-xxLpjg87JgWvBX5ISXpZrpHDp69j4m5-N4x_JRRkqp3pBjKQ0XgusTcrNcfLldfaLLCMOGLnYpYO-Cx0gXwxADuA1tQqQ5h36P6FuX2tDTyxi29AoTxjB1EXYj_Ybpd4g_35LXDXQjnj_eZ-TH5dfbi6vZ9c1ydbG4njlVFmkGQnlQzBhvPFeyaoxypjAATCN64bnPjbsKHSiQNatN00Ae2oGpkFeslGdkdeD6AHd2iO0W4h8boLX7QIhrCzG1rkMrTY2IXHjDlRK1NEIwV2LZlFyjli6zPh9Yw67eonfYpwjdC-jLn77d2HW4t5pVQmiZAR8eATH82uGY7LYdHXYd7HdjRcFNZUyhpr7fP9f6K_LkSE6oDgkuhnGM2FjXJpi2nqXbznJmJ_vt3n472W8P9udK_k_lE_z_NQ_WwbNE |
| CitedBy_id | crossref_primary_10_1109_JBHI_2023_3240305 crossref_primary_10_1016_j_artmed_2024_102778 crossref_primary_10_1016_j_ymeth_2024_01_018 crossref_primary_10_1186_s12915_025_02231_w crossref_primary_10_1038_s41598_024_76367_0 crossref_primary_10_1089_cmb_2023_0135 crossref_primary_10_1109_TCBB_2023_3339189 crossref_primary_10_1186_s12859_023_05275_3 crossref_primary_10_1186_s12859_025_06075_7 crossref_primary_10_1007_s12539_025_00697_4 crossref_primary_10_1186_s12859_023_05496_6 crossref_primary_10_1186_s12859_025_06198_x crossref_primary_10_1109_ACCESS_2022_3199667 crossref_primary_10_1186_s12915_025_02340_6 crossref_primary_10_3389_fgene_2021_718915 crossref_primary_10_1186_s12859_022_05119_6 crossref_primary_10_1039_D4MO00117F |
| Cites_doi | 10.1038/msb.2009.98 10.1145/2487575.2487670 10.1109/TCBB.2019.2936476 10.1093/nar/gks994 10.2174/1389203721666200702145701 10.1101/539643 10.1016/j.neucom.2016.03.080 10.1021/acs.jproteome.6b00618 10.1093/bioinformatics/bts670 10.1145/2623330.2623732 10.3389/fgene.2018.00248 10.1093/bioinformatics/btz111 10.3389/fbioe.2019.00305 10.1021/ci100050t 10.1039/c2mb00002d 10.1093/bioinformatics/btu403 10.1093/nar/gkn892 10.1093/bioinformatics/btx160 10.1093/bib/bby117 10.1109/TKDE.2018.2807452 10.1016/j.ygeno.2018.12.007 10.1186/s13321-015-0089-z 10.3390/jpm10030128 10.1093/bioinformatics/bty543 10.1093/bioinformatics/btx731 10.1093/bioinformatics/btz600 10.1371/journal.pcbi.1000397 10.1016/0022-2836(81)90087-5 10.1145/3219819.3219890 10.1093/bioinformatics/bty294 10.1109/TCBB.2020.3034910 10.1021/acs.jproteome.9b00411 10.1609/aaai.v32i1.11604 10.1007/s10115-007-0094-2 10.1007/s10822-016-9938-8 10.1371/journal.pcbi.1002503 10.1093/nar/gkq1126 10.1038/s41467-017-00680-8 10.1093/bib/bbz157 10.1093/bioinformatics/bty440 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Liu, Chen, Lan, Pan, Hao and Pan. Copyright © 2021 Liu, Chen, Lan, Pan, Hao and Pan. 2021 Liu, Chen, Lan, Pan, Hao and Pan |
| Copyright_xml | – notice: Copyright © 2021 Liu, Chen, Lan, Pan, Hao and Pan. – notice: Copyright © 2021 Liu, Chen, Lan, Pan, Hao and Pan. 2021 Liu, Chen, Lan, Pan, Hao and Pan |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.3389/fgene.2021.650821 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1664-8021 |
| ExternalDocumentID | oai_doaj_org_article_39beee12d91442b39220c6e6f618e83c PMC8072283 33912218 10_3389_fgene_2021_650821 |
| Genre | Journal Article |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ACGFS ADBBV ADRAZ AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV CITATION DIK EMOBN GROUPED_DOAJ GX1 HYE KQ8 M48 M~E OK1 PGMZT RNS RPM ACXDI IPNFZ NPM RIG 7X8 5PM |
| ID | FETCH-LOGICAL-c465t-a24da4099d9d1437f94c959aa08eed2d1d021c7eca4a3b0b9ffa389ca97e17063 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 33 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000643698200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1664-8021 |
| IngestDate | Fri Oct 03 12:53:36 EDT 2025 Thu Aug 21 18:23:12 EDT 2025 Thu Sep 04 17:28:36 EDT 2025 Thu Apr 03 06:58:09 EDT 2025 Tue Nov 18 22:13:48 EST 2025 Sat Nov 29 03:02:19 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | graph convolutional network random walk drug-target interaction prediction autoencoder heterogeneous network network embedding |
| Language | English |
| License | Copyright © 2021 Liu, Chen, Lan, Pan, Hao and Pan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c465t-a24da4099d9d1437f94c959aa08eed2d1d021c7eca4a3b0b9ffa389ca97e17063 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Xiangxiang Zeng, Hunan University, China This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics Reviewed by: Khanh N. Q. Le, Taipei Medical University, Taiwan; Yusen Zhang, Shandong University, China |
| OpenAccessLink | https://doaj.org/article/39beee12d91442b39220c6e6f618e83c |
| PMID | 33912218 |
| PQID | 2519799546 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_39beee12d91442b39220c6e6f618e83c pubmedcentral_primary_oai_pubmedcentral_nih_gov_8072283 proquest_miscellaneous_2519799546 pubmed_primary_33912218 crossref_citationtrail_10_3389_fgene_2021_650821 crossref_primary_10_3389_fgene_2021_650821 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-04-09 |
| PublicationDateYYYYMMDD | 2021-04-09 |
| PublicationDate_xml | – month: 04 year: 2021 text: 2021-04-09 day: 09 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in genetics |
| PublicationTitleAlternate | Front Genet |
| PublicationYear | 2021 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | B23 Tong (B39) 2008; 14 Wen (B42) 2017; 16 Rogers (B34) 2010; 50 Seal (B35) 2015; 7 Ma (B26) 2018 Wan (B40) 2019; 35 Su (B38) 2018; 21 Smith (B37) 1981; 147 Cai (B2) 2017; 30 Pan (B32) 2018 Ying (B45) 2018 Luo (B25) 2017; 8 B31 Gligorijevic (B7) 2018; 33 Perozzi (B33) 2014 Davis (B6) 2013; 41 Goodfellow (B8) 2014 Grover (B9) 2016 Zong (B49) 2017; 33 Zitnik (B48) 2018; 34 Cheng (B5) 2012; 8 Le (B22) 2019; 7 Jin (B10) 2019 B44 Mohamed (B28) 2019; 36 Knox (B16) 2011; 39 Lan (B18) 2020 Le (B20) 2019; 18 Mei (B27) 2013; 29 Chen (B3) 2019 Shi (B36) 2019; 111 B50 Karimi (B11) 2019; 35 Xu (B43) 2018 Kearnes (B12) 2016; 30 B14 B15 Zheng (B46) 2013 Zhu (B47) 2018; 9 Lan (B19) 2016; 206 Liu (B24) 2020; 21 Chen (B4) 2012; 8 Bagherian (B1) 2020; 22 Wang (B41) 2014; 30 Le (B21) 2020; 10 Kuhn (B17) 2010; 6 Nagamine (B29) 2009; 5 Olayan (B30) 2018; 34 Keshava Prasad (B13) 2009; 37 |
| References_xml | – volume: 6 start-page: 343 year: 2010 ident: B17 article-title: A side effect resource to capture phenotypic effects of drugs publication-title: Mol. Syst. Biol. doi: 10.1038/msb.2009.98 – start-page: 2609 volume-title: The 27th International Joint Conference on Artificial Intelligence year: 2018 ident: B32 article-title: Adversarially regularized graph autoencoder for graph embedding, – start-page: 1025 volume-title: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining year: 2013 ident: B46 article-title: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions, doi: 10.1145/2487575.2487670 – year: 2019 ident: B3 article-title: ILDMSF: inferring associations between long non-coding RNA and disease based on multi-similarity fusion publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2019.2936476 – volume: 41 start-page: D1104 year: 2013 ident: B6 article-title: The comparative toxicogenomics database: update 2013 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gks994 – volume: 21 start-page: 1 year: 2020 ident: B24 article-title: A survey of network embedding for drug analysis and prediction publication-title: Curr. Protein Peptide Sci. doi: 10.2174/1389203721666200702145701 – ident: B50 doi: 10.1101/539643 – volume: 206 start-page: 50 year: 2016 ident: B19 article-title: Predicting drug-target interaction using positive-unlabeled learning publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.03.080 – volume-title: The 7th International Conference on Learning Representations: OpenReview.net year: 2019 ident: B10 article-title: Learning multimodal graph-to-graph translation for molecular optimization, – volume: 16 start-page: 1401 year: 2017 ident: B42 article-title: Deep-learning-based drug-target interaction prediction publication-title: J. Proteome Res. doi: 10.1021/acs.jproteome.6b00618 – ident: B31 – volume: 29 start-page: 238 year: 2013 ident: B27 article-title: Drug-target interaction prediction by learning from local information and neighbors publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts670 – start-page: 701 volume-title: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining year: 2014 ident: B33 article-title: DeepWalk: online learning of social representations, doi: 10.1145/2623330.2623732 – volume: 9 start-page: 248 year: 2018 ident: B47 article-title: Prediction of drug-gene interaction by Using Metapath2vec publication-title: Front. Genet. doi: 10.3389/fgene.2018.00248 – volume: 35 start-page: 3329 year: 2019 ident: B11 article-title: DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz111 – volume: 7 start-page: 305 year: 2019 ident: B22 article-title: Classifying promoters by interpreting the hidden information of DNA sequences via deep learning and combination of continuous fasttext N-grams publication-title: Front. Bioeng. Biotechnol. doi: 10.3389/fbioe.2019.00305 – volume: 50 start-page: 742 year: 2010 ident: B34 article-title: Extended-connectivity fingerprints publication-title: J Chem Inf Model doi: 10.1021/ci100050t – ident: B44 – volume: 8 start-page: 1970 year: 2012 ident: B4 article-title: Drug-target interaction prediction by random walk on the heterogeneous network publication-title: Mol. Biosyst. doi: 10.1039/c2mb00002d – volume: 30 start-page: 2923 year: 2014 ident: B41 article-title: Drug repositioning by integrating target information through a heterogeneous network model publication-title: Bioinformatics doi: 10.1093/bioinformatics/btu403 – volume: 37 start-page: D767 year: 2009 ident: B13 article-title: Human protein reference database 2009 update publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkn892 – volume: 33 start-page: 2337 year: 2017 ident: B49 article-title: Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx160 – volume: 21 start-page: 182 year: 2018 ident: B38 article-title: Network embedding in biomedical data science publication-title: Brief. Bioinform. doi: 10.1093/bib/bby117 – ident: B14 – volume: 30 start-page: 1616 year: 2017 ident: B2 article-title: A comprehensive survey of graph embedding: problems, techniques and applications publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2018.2807452 – volume: 111 start-page: 1839 year: 2019 ident: B36 article-title: Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure publication-title: Genomics doi: 10.1016/j.ygeno.2018.12.007 – volume: 7 start-page: 40 year: 2015 ident: B35 article-title: Optimizing drug-target interaction prediction based on random walk on heterogeneous networks publication-title: J. Cheminform. doi: 10.1186/s13321-015-0089-z – volume: 10 start-page: 128 year: 2020 ident: B21 article-title: XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma publication-title: J Pers. Med. doi: 10.3390/jpm10030128 – volume: 35 start-page: 104 year: 2019 ident: B40 article-title: NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty543 – volume: 34 start-page: 1164 year: 2018 ident: B30 article-title: DDR: Efficient computational method to predict drug-target interactions using graph mining and machine learning approaches publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx731 – volume: 36 start-page: 603 year: 2019 ident: B28 article-title: Discovering protein drug targets using knowledge graph embeddings publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz600 – volume: 5 start-page: e1000397 year: 2009 ident: B29 article-title: Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening publication-title: PLOS Comput. Biol. doi: 10.1371/journal.pcbi.1000397 – volume: 147 start-page: 195 year: 1981 ident: B37 article-title: Identification of common molecular subsequences publication-title: J. Mol. Biol. doi: 10.1016/0022-2836(81)90087-5 – start-page: 974 volume-title: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining year: 2018 ident: B45 article-title: Graph convolutional neural networks for web-scale recommender systems, doi: 10.1145/3219819.3219890 – volume: 34 start-page: 457 year: 2018 ident: B48 article-title: Modeling polypharmacy side effects with graph convolutional networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty294 – year: 2020 ident: B18 article-title: LDICDL: LncRNA-disease association identification based on Collaborative Deep Learning publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2020.3034910 – start-page: 5449 volume-title: The 35th International Conference on Machine Learning year: 2018 ident: B43 article-title: Representation learning on graphs with jumping knowledge networks, – volume: 18 start-page: 3503 year: 2019 ident: B20 article-title: Fertility-GRU: identifying fertility-related proteins by incorporating deep-gated recurrent units and original position-specific scoring matrix profiles publication-title: J Proteome Res doi: 10.1021/acs.jproteome.9b00411 – ident: B23 doi: 10.1609/aaai.v32i1.11604 – volume: 14 start-page: 327 year: 2008 ident: B39 article-title: Random walk with restart: fast solutions and applications publication-title: Knowl. Inform. Syst. doi: 10.1007/s10115-007-0094-2 – start-page: 855 volume-title: Conference on Knowledge Discovery and Data Mining year: 2016 ident: B9 article-title: node2vec: Scalable Feature Learning for Networks, – volume: 30 start-page: 1 year: 2016 ident: B12 article-title: Molecular graph convolutions: moving beyond fingerprints publication-title: J. Comput. Aided Mol. Des. doi: 10.1007/s10822-016-9938-8 – volume: 8 start-page: e1002503 year: 2012 ident: B5 article-title: Prediction of drug-target interactions and drug repositioning via network-based inference publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1002503 – start-page: 3477 volume-title: The 27th International Joint Conference on Artificial Intelligence year: 2018 ident: B26 article-title: Drug similarity integration through attentive multi-view graph auto-encoders, – volume-title: Proceedings of the 27th International Conference on Neural Information Processing Systems year: 2014 ident: B8 article-title: Generative adversarial nets, – volume: 39 start-page: D1035 year: 2011 ident: B16 article-title: DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkq1126 – volume: 8 start-page: 573 year: 2017 ident: B25 article-title: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information publication-title: Nat. Commun. doi: 10.1038/s41467-017-00680-8 – ident: B15 – volume: 22 start-page: 247 year: 2020 ident: B1 article-title: Machine learning approaches and databases for prediction of drug-target interaction: a survey paper publication-title: Brief. Bioinform. doi: 10.1093/bib/bbz157 – volume: 33 start-page: 3873 year: 2018 ident: B7 article-title: deepNF: Deep network fusion for protein function prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty440 |
| SSID | ssj0000493334 |
| Score | 2.4079034 |
| Snippet | Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target... Identifying drug-target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 650821 |
| SubjectTerms | autoencoder drug-target interaction prediction Genetics graph convolutional network heterogeneous network network embedding random walk |
| Title | GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33912218 https://www.proquest.com/docview/2519799546 https://pubmed.ncbi.nlm.nih.gov/PMC8072283 https://doaj.org/article/39beee12d91442b39220c6e6f618e83c |
| Volume | 12 |
| WOSCitedRecordID | wos000643698200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1664-8021 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000493334 issn: 1664-8021 databaseCode: DOA dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1664-8021 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000493334 issn: 1664-8021 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB1BBRIXxDfhozISJ6TQxPbaGW4LdFsOLD0UaW-RYzuiiG6qdBeJC7-dmThd7SIEFy45JI4yeTPOPMuTNwAvg_IoK6Nzh97kutIxx4kNuSduKh1lEOlTswk7n1eLBZ5stfrimrAkD5yAO1DYxBhLGZCov2woncvCm2haU1axUp6_voXFrcXU18R7lVI6bWPSKgwPWvIHy2LK8jWTElnuJKJBr_9PJPP3Wsmt5DO7A7dH1iimydq7cC0u78HN1Efyx334dDR9f_rhjThi9WkxXa86lqcMsRfTUTJcEDcVNEac9Lwzw94Qs747F8dcDtOx1d36UsxTUfgD-Dw7PH13nI-dEnKvzWSVO6kDAYsYMBABsi1qjxN0rqgoB8pQBnpvb6N32qmmaLBtHYHiHdrI-jnqIewtu2V8DMJHbIsyKuNd1K3WDc1Y206M1z40zjQZFFew1X6UEeduFt9qWk4w0vWAdM1I1wnpDF5tbrlIGhp_G_yWfbEZyPLXwwkKinoMivpfQZHBiytP1jRdeA_EDTjWw4-6rIFnMniUPLt5lFJYSqI8Gdgdn-_YsntlefZlkOSuCss6Qk_-h_FP4RbjMZQH4TPYW_Xr-Bxu-O-rs8t-H67bRbU_RDsdP_48_AUjNASl |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=GADTI%3A+Graph+Autoencoder+Approach+for+DTI+Prediction+From+Heterogeneous+Network&rft.jtitle=Frontiers+in+genetics&rft.au=Liu%2C+Zhixian&rft.au=Chen%2C+Qingfeng&rft.au=Lan%2C+Wei&rft.au=Pan%2C+Haiming&rft.date=2021-04-09&rft.issn=1664-8021&rft.eissn=1664-8021&rft.volume=12&rft.spage=650821&rft_id=info:doi/10.3389%2Ffgene.2021.650821&rft_id=info%3Apmid%2F33912218&rft.externalDocID=33912218 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-8021&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-8021&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-8021&client=summon |