Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines
Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accu...
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| Vydáno v: | Frontiers in pharmacology Ročník 15; s. 1529128 |
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| Jazyk: | angličtina |
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Frontiers Media S.A
06.01.2025
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| ISSN: | 1663-9812, 1663-9812 |
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| Abstract | Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model’s effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in:
https://github.com/shmildsj/data
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| AbstractList | Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model’s effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data. Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model's effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data.Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model's effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data. Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model’s effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data . |
| Author | Chen, Yangyuan Ma, Hongming Gao, Hong Wang, Hongwu Zhang, Nan Wang, Zhina Zhu, Yangbin |
| AuthorAffiliation | 1 Department of Pulmonary and Critical Care Medicine II , Emergency General Hospital , Beijing , China 3 School of Data Science and Artificial Intelligence , Wenzhou University of Technology , Wenzhou , China 4 Respiratory Disease Center , Dongzhimen Hospital , Beijing University of Chinese Medicine , Beijing , China 2 Department of Oncology , Emergency General Hospital , Beijing , China |
| AuthorAffiliation_xml | – name: 4 Respiratory Disease Center , Dongzhimen Hospital , Beijing University of Chinese Medicine , Beijing , China – name: 1 Department of Pulmonary and Critical Care Medicine II , Emergency General Hospital , Beijing , China – name: 2 Department of Oncology , Emergency General Hospital , Beijing , China – name: 3 School of Data Science and Artificial Intelligence , Wenzhou University of Technology , Wenzhou , China |
| Author_xml | – sequence: 1 givenname: Zhina surname: Wang fullname: Wang, Zhina – sequence: 2 givenname: Yangyuan surname: Chen fullname: Chen, Yangyuan – sequence: 3 givenname: Hongming surname: Ma fullname: Ma, Hongming – sequence: 4 givenname: Hong surname: Gao fullname: Gao, Hong – sequence: 5 givenname: Yangbin surname: Zhu fullname: Zhu, Yangbin – sequence: 6 givenname: Hongwu surname: Wang fullname: Wang, Hongwu – sequence: 7 givenname: Nan surname: Zhang fullname: Zhang, Nan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39834830$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1080/15476286.2020.1737441 10.1093/bib/bbad129 10.1093/bib/bbae127 10.1093/nar/gkac814 10.1093/bib/bbac339 10.1186/s13046-015-0170-5 10.1016/j.bbcan.2012.03.005 10.1109/CVPR.2018.00534 10.1093/bib/bbae179 10.1093/bib/bbae078 10.18653/v1/2020.emnlp-main.574 10.1038/srep18614 10.1016/s0092-8674(00)81308-2 10.1016/j.omtn.2024.102187 10.1186/s12915-024-02028-3 10.1016/j.compbiomed.2023.107143 10.1186/1476-4598-9-198 10.1016/j.omtn.2023.102103 10.1007/s12094-024-03606-1 10.1093/bib/bbad227 10.1109/JBHI.2024.3496294 10.1093/bib/bbad483 10.1371/journal.pcbi.1010671 10.1093/bib/bbad247 10.1109/TCBB.2022.3204726 10.1093/bfgp/elae005 10.1145/3589334.3645601 10.1109/CVPR.2017.713 10.1093/nar/gkaa707 10.1093/bib/bbad094 10.1093/bib/bbac240 10.1016/j.ijbiomac.2024.133825 10.1016/j.gendis.2022.11.018 10.1109/jbhi.2024.3510297 10.1109/JBHI.2021.3088342 10.1093/bioinformatics/btae271 10.1016/j.compbiomed.2024.108104 10.1145/3459637.3482215 10.1126/science.1118265 10.1016/j.future.2024.05.055 10.1021/acs.jpclett.4c01509 10.1371/journal.pcbi.1012400 10.1186/s12864-023-09802-7 10.1016/j.compbiomed.2024.108484 10.1371/journal.pone.0162622 10.1021/acs.jcim.3c00868 10.1016/0169-7439(89)80095-4 10.1038/nrg3074 10.1016/j.compbiomed.2023.106783 10.3389/fmicb.2023.1170559 10.1093/bioinformatics/btz965 10.1038/s41540-023-00267-8 10.1093/bioinformatics/btae438 10.3389/fonc.2019.00788 |
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| Keywords | lung cancer snoRNA therapeutic targets redevelopment of traditional medicines artificial intelligence (AI) variational graph autoencoder (VGAE) |
| Language | English |
| License | Copyright © 2025 Wang, Chen, Ma, Gao, Zhu, Wang and Zhang. 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. |
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| References | Sun (B31) 2022; 23 Wang (B34); 276 Wei (B43); 157 Zhao (B50) 2022; 20 Esteller (B5) 2011; 12 Wang (B32); 25 Hou (B6) 2022; 18 Kipf (B8) 2016 Liu (B20) 2017 Zhang (B46); 17 Ranjan (B29) 2017 Zheng (B52) 2018 Zhang (B48) 2023; 10 Zhou (B56); 35 Ning (B26) 2021; 49 Ning (B27) 2023; 24 Zhang (B47); 35 Ma (B23); 40 Zhuo (B59) 2023; 24 Cho (B3) 2024 Liu (B22) 2024 Liu (B21); 24 Qi (B28) 2016; 6 Li (B13) 2020; 36 Liu (B19); 24 Krishnan (B12) 2016; 11 Kishore (B9) 2006; 311 Kiss-László (B10) 1996; 85 Zhou (B54) 2023; 163 Zheng (B51) 2015; 34 Chen (B2) 2023; 51 Liao (B17) 2010; 9 Wei (B44); 24 Zhang (B45); 33 Zhou (B57); 25 Mannoor (B25) 2012; 1826 Zhuo (B58) 2022; 23 Wang (B35); 25 Ahn (B1) 2021 Ma (B24); 174 Wei (B40) Ding (B4) 2021; 26 Wei (B41); 15 Zhou (B55); 40 Wei (B39); 171 Li (B15) 2023; 9 Liao (B18) 2023; 14 Zhang (B49) St (B30) 1989; 6 Zhou (B53); 23 Wang (B33); 64 Wang (B37) 2019; 9 Hu (B7) 2024; 25 Li (B16); 160 Wang (B36); 20 Wang (B38) Kobayashi (B11) 2020 Wei (B42); 22 Li (B14) |
| References_xml | – volume: 17 start-page: 943 ident: B46 article-title: ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs publication-title: RNA Biol. doi: 10.1080/15476286.2020.1737441 – volume: 24 start-page: bbad129 ident: B21 article-title: NSRGRN: a network structure refinement method for gene regulatory network inference publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbad129 – volume-title: L2-constrained softmax loss for discriminative face verification year: 2017 ident: B29 – volume: 25 start-page: bbae127 ident: B35 article-title: MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbae127 – volume: 51 start-page: D1397 year: 2023 ident: B2 article-title: RNADisease v4. 0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkac814 – volume: 23 start-page: bbac339 year: 2022 ident: B58 article-title: Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learning publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbac339 – volume: 34 start-page: 49 year: 2015 ident: B51 article-title: Small nucleolar RNA 78 promotes the tumorigenesis in non-small cell lung cancer publication-title: J. Exp. and Clin. Cancer Res. doi: 10.1186/s13046-015-0170-5 – volume: 1826 start-page: 121 year: 2012 ident: B25 article-title: Small nucleolar RNAs in cancer publication-title: Biochimica Biophysica Acta (BBA)-Reviews Cancer doi: 10.1016/j.bbcan.2012.03.005 – volume-title: Proceedings of the IEEE conference on computer vision and pattern recognition year: 2018 ident: B52 article-title: Ring loss: convex feature normalization for face recognition doi: 10.1109/CVPR.2018.00534 – volume: 25 start-page: bbae179 year: 2024 ident: B7 article-title: IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbae179 – volume-title: Variational graph auto-encoders year: 2016 ident: B8 – volume: 25 start-page: bbae078 ident: B32 article-title: Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbae078 – volume-title: 2020 conference on empirical methods in natural language processing, EMNLP 2020 year: 2020 ident: B11 article-title: Attention is not only a weight: analyzing transformers with vector norms doi: 10.18653/v1/2020.emnlp-main.574 – volume: 6 start-page: 18614 year: 2016 ident: B28 article-title: Snord116 is critical in the regulation of food intake and body weight publication-title: Sci. Rep. doi: 10.1038/srep18614 – volume: 85 start-page: 1077 year: 1996 ident: B10 article-title: Site-specific ribose methylation of preribosomal RNA: a novel function for small nucleolar RNAs publication-title: Cell doi: 10.1016/s0092-8674(00)81308-2 – volume: 35 start-page: 102187 ident: B47 article-title: Fusion of multi-source relationships and topology to infer lncRNA-protein interactions publication-title: Mol. Therapy-Nucleic Acids doi: 10.1016/j.omtn.2024.102187 – volume: 22 start-page: 226 ident: B42 article-title: DrugReAlign: a multisource prompt framework for drug repurposing based on large language models publication-title: BMC Biol. doi: 10.1186/s12915-024-02028-3 – volume: 163 start-page: 107143 year: 2023 ident: B54 article-title: MHAM-NPI: predicting ncRNA-protein interactions based on multi-head attention mechanism publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2023.107143 – volume: 9 start-page: 198 year: 2010 ident: B17 article-title: Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer publication-title: Mol. cancer doi: 10.1186/1476-4598-9-198 – volume: 35 start-page: 102103 ident: B56 article-title: Joint masking and self-supervised strategies for inferring small molecule-miRNA associations publication-title: Mol. Therapy-Nucleic Acids doi: 10.1016/j.omtn.2023.102103 – start-page: 1 ident: B14 article-title: The diagnostic value of serum exosomal SNORD116 and SNORA21 for NSCLC patients publication-title: Clin. Transl. Oncol. doi: 10.1007/s12094-024-03606-1 – volume: 24 start-page: bbad227 ident: B19 article-title: MPCLCDA: predicting circRNA–disease associations by using automatically selected meta-path and contrastive learning publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbad227 – start-page: 1 ident: B40 article-title: BloodPatrol: revolutionizing blood cancer diagnosis - advanced real-time detection leveraging deep learning and cloud technologies publication-title: IEEE J. Biomed. health Inf. doi: 10.1109/JBHI.2024.3496294 – volume: 25 start-page: bbad483 ident: B57 article-title: Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbad483 – volume: 18 start-page: e1010671 year: 2022 ident: B6 article-title: iPiDA-GCN: identification of piRNA-disease associations based on Graph Convolutional Network publication-title: PLOS Comput. Biol. doi: 10.1371/journal.pcbi.1010671 – volume: 24 start-page: bbad247 ident: B44 article-title: GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbad247 – volume: 20 start-page: 1298 year: 2022 ident: B50 article-title: Predicting Mirna-disease associations based on neighbor selection graph attention networks publication-title: IEEE/ACM Trans. Comput. Biol. Bioinforma. doi: 10.1109/TCBB.2022.3204726 – volume: 23 start-page: 475 ident: B53 article-title: GAM-MDR: probing miRNA-drug resistance using a graph autoencoder based on random path masking publication-title: Briefings Funct. Genomics doi: 10.1093/bfgp/elae005 – volume-title: Proceedings of the ACM on web conference 2024 year: 2024 ident: B3 article-title: Decoupled variational graph autoencoder for link prediction doi: 10.1145/3589334.3645601 – volume: 33 start-page: 18772 ident: B45 article-title: Deep metric learning with spherical embedding publication-title: Adv. Neural Inf. Process. Syst. – volume-title: Proceedings of the IEEE conference on computer vision and pattern recognition year: 2017 ident: B20 article-title: Sphereface: deep hypersphere embedding for face recognition doi: 10.1109/CVPR.2017.713 – volume: 49 start-page: D160 year: 2021 ident: B26 article-title: MNDR v3. 0: mammal ncRNA–disease repository with increased coverage and annotation publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkaa707 – volume: 24 start-page: bbad094 year: 2023 ident: B27 article-title: AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA–disease associations identification publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbad094 – volume: 23 start-page: bbac240 year: 2022 ident: B31 article-title: PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization publication-title: Briefings Bioinforma. doi: 10.1093/bib/bbac240 – volume: 276 start-page: 133825 ident: B34 article-title: MultiCBlo: enhancing predictions of compound-induced inhibition of cardiac ion channels with advanced multimodal learning publication-title: Int. J. Biol. Macromol. doi: 10.1016/j.ijbiomac.2024.133825 – volume: 10 start-page: 2064 year: 2023 ident: B48 article-title: The emerging role of snoRNAs in human disease publication-title: Genes and Dis. doi: 10.1016/j.gendis.2022.11.018 – start-page: 1 ident: B49 article-title: CardiOT: towards interpretable drug cardiotoxicity prediction using optimal transport and Kolmogorov-arnold networks publication-title: IEEE J. Biomed. health Inf. doi: 10.1109/jbhi.2024.3510297 – volume-title: SBSM-pro: support bio-sequence machine for proteins ident: B38 – volume: 26 start-page: 446 year: 2021 ident: B4 article-title: Predicting miRNA-disease associations based on multi-view variational graph auto-encoder with matrix factorization publication-title: IEEE J. Biomed. health Inf. doi: 10.1109/JBHI.2021.3088342 – volume: 40 start-page: btae271 ident: B55 article-title: Revisiting drug–protein interaction prediction: a novel global–local perspective publication-title: Bioinformatics doi: 10.1093/bioinformatics/btae271 – volume: 171 start-page: 108104 ident: B39 article-title: Enhancing drug–food interaction prediction with precision representations through multilevel self-supervised learning publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2024.108104 – volume-title: Proceedings of the 30th ACM international conference on information and knowledge management year: 2021 ident: B1 article-title: Variational graph normalized autoencoders doi: 10.1145/3459637.3482215 – volume: 311 start-page: 230 year: 2006 ident: B9 article-title: The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C publication-title: science doi: 10.1126/science.1118265 – volume-title: Kan: Kolmogorov-arnold networks year: 2024 ident: B22 – volume: 160 start-page: 109 ident: B16 article-title: Multi-source data integration for explainable miRNA-driven drug discovery publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2024.05.055 – volume: 15 start-page: 7681 ident: B41 article-title: Efficient deep model ensemble framework for drug-target interaction prediction publication-title: J. Phys. Chem. Lett. doi: 10.1021/acs.jpclett.4c01509 – volume: 20 start-page: e1012400 ident: B36 article-title: ECD-CDGI: an efficient energy-constrained diffusion model for cancer driver gene identification publication-title: PLOS Comput. Biol. doi: 10.1371/journal.pcbi.1012400 – volume: 24 start-page: 742 year: 2023 ident: B59 article-title: StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning publication-title: BMC genomics doi: 10.1186/s12864-023-09802-7 – volume: 174 start-page: 108484 ident: B24 article-title: Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2024.108484 – volume: 11 start-page: e0162622 year: 2016 ident: B12 article-title: Profiling of small nucleolar RNAs by next generation sequencing: potential new players for breast cancer prognosis publication-title: PloS one doi: 10.1371/journal.pone.0162622 – volume: 64 start-page: 2798 ident: B33 article-title: An effective plant small secretory peptide recognition model based on feature correction strategy publication-title: J. Chem. Inf. Model. doi: 10.1021/acs.jcim.3c00868 – volume: 6 start-page: 259 year: 1989 ident: B30 article-title: Analysis of variance (ANOVA) publication-title: Chemom. intelligent laboratory Syst. doi: 10.1016/0169-7439(89)80095-4 – volume: 12 start-page: 861 year: 2011 ident: B5 article-title: Non-coding RNAs in human disease publication-title: Nat. Rev. Genet. doi: 10.1038/nrg3074 – volume: 157 start-page: 106783 ident: B43 article-title: Headtailtransfer: an efficient sampling method to improve the performance of graph neural network method in predicting sparse ncrna–protein interactions publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2023.106783 – volume: 14 start-page: 1170559 year: 2023 ident: B18 article-title: Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders publication-title: Front. Microbiol. doi: 10.3389/fmicb.2023.1170559 – volume: 36 start-page: 2538 year: 2020 ident: B13 article-title: Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz965 – volume: 9 start-page: 4 year: 2023 ident: B15 article-title: Integration of single sample and population analysis for understanding immune evasion mechanisms of lung cancer publication-title: NPJ Syst. Biol. Appl. doi: 10.1038/s41540-023-00267-8 – volume: 40 start-page: btae438 ident: B23 article-title: GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remapping publication-title: Bioinformatics doi: 10.1093/bioinformatics/btae438 – volume: 9 start-page: 788 year: 2019 ident: B37 article-title: Identification of eight small nucleolar RNAs as survival biomarkers and their clinical significance in gastric cancer publication-title: Front. Oncol. doi: 10.3389/fonc.2019.00788 |
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| SubjectTerms | artificial intelligence (AI) lung cancer Pharmacology redevelopment of traditional medicines snoRNA therapeutic targets variational graph autoencoder (VGAE) |
| Title | Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
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