Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learning Article Swipe
Linlin Zhuo
,
Bosheng Song
,
yuansheng liu
,
Zejun Li
,
Xiangzheng Fu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbac339
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbac339
Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA–proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, diseases, etc. Traditional experimental methods can accomplish this work but are often labor-intensive and expensive. Machine learning and deep learning methods have achieved great success by exploiting sufficient sequence or structure information. Graph Neural Network (GNN)-based methods consider the topology in ncRNA–protein graphs and perform well on tasks like NPI prediction. Based on GNN, some pairwise constraint methods have been developed to apply on homogeneous networks, but not used for NPI prediction on heterogeneous networks. In this paper, we construct a pairwise constrained NPI predictor based on dual Graph Convolutional Network (GCN) called NPI-DGCN. To our knowledge, our method is the first to train a heterogeneous graph-based model using a pairwise learning strategy. Instead of binary classification, we use a rank layer to calculate the score of an ncRNA–protein pair. Moreover, our model is the first to predict NPIs on the ncRNA–protein bipartite graph rather than the homogeneous graph. We transform the original ncRNA–protein bipartite graph into two homogenous graphs on which to explore second-order implicit relationships. At the same time, we model direct interactions between two homogenous graphs to explore explicit relationships. Experimental results on the four standard datasets indicate that our method achieves competitive performance with other state-of-the-art methods. And the model is available at https://github.com/zhuoninnin1992/NPIPredict
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbac339
- https://academic.oup.com/bib/article-pdf/23/6/bbac339/48076007/bbac339.pdf
- OA Status
- bronze
- Cited By
- 18
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294873403
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4294873403Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bib/bbac339Digital Object Identifier
- Title
-
Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-31Full publication date if available
- Authors
-
Linlin Zhuo, Bosheng Song, yuansheng liu, Zejun Li, Xiangzheng FuList of authors in order
- Landing page
-
https://doi.org/10.1093/bib/bbac339Publisher landing page
- PDF URL
-
https://academic.oup.com/bib/article-pdf/23/6/bbac339/48076007/bbac339.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/bib/article-pdf/23/6/bbac339/48076007/bbac339.pdfDirect OA link when available
- Concepts
-
Pairwise comparison, Computer science, Bipartite graph, Non-coding RNA, Graph, Artificial intelligence, Theoretical computer science, Machine learning, Dual (grammatical number), Biology, RNA, Gene, Literature, Art, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 4, 2023: 7Per-year citation counts (last 5 years)
- References (count)
-
58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4294873403 |
|---|---|
| doi | https://doi.org/10.1093/bib/bbac339 |
| ids.doi | https://doi.org/10.1093/bib/bbac339 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/36063562 |
| ids.openalex | https://openalex.org/W4294873403 |
| fwci | 2.44721729 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000069550 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Machine Learning |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D016571 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Neural Networks, Computer |
| mesh[2].qualifier_ui | Q000737 |
| mesh[2].descriptor_ui | D011506 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | chemistry |
| mesh[2].descriptor_name | Proteins |
| mesh[3].qualifier_ui | Q000235 |
| mesh[3].descriptor_ui | D022661 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | genetics |
| mesh[3].descriptor_name | RNA, Untranslated |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000069550 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Machine Learning |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D016571 |
| mesh[5].is_major_topic | True |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Neural Networks, Computer |
| mesh[6].qualifier_ui | Q000737 |
| mesh[6].descriptor_ui | D011506 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | chemistry |
| mesh[6].descriptor_name | Proteins |
| mesh[7].qualifier_ui | Q000235 |
| mesh[7].descriptor_ui | D022661 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | genetics |
| mesh[7].descriptor_name | RNA, Untranslated |
| type | article |
| title | Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learning |
| awards[0].id | https://openalex.org/G8012246451 |
| awards[0].funder_id | https://openalex.org/F4320306076 |
| awards[0].display_name | |
| awards[0].funder_award_id | # 62002111 |
| awards[0].funder_display_name | National Science Foundation |
| biblio.issue | 6 |
| biblio.volume | 23 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10515 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1306 |
| topics[0].subfield.display_name | Cancer Research |
| topics[0].display_name | Cancer-related molecular mechanisms research |
| topics[1].id | https://openalex.org/T11482 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9998000264167786 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | RNA modifications and cancer |
| topics[2].id | https://openalex.org/T10604 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9998000264167786 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | RNA Research and Splicing |
| funders[0].id | https://openalex.org/F4320306076 |
| funders[0].ror | https://ror.org/021nxhr62 |
| funders[0].display_name | National Science Foundation |
| is_xpac | False |
| apc_list.value | 4011 |
| apc_list.currency | USD |
| apc_list.value_usd | 4011 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C184898388 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7483431100845337 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1435712 |
| concepts[0].display_name | Pairwise comparison |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6460825800895691 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C197657726 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5199650526046753 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q174733 |
| concepts[2].display_name | Bipartite graph |
| concepts[3].id | https://openalex.org/C194993378 |
| concepts[3].level | 4 |
| concepts[3].score | 0.5147395133972168 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q427087 |
| concepts[3].display_name | Non-coding RNA |
| concepts[4].id | https://openalex.org/C132525143 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5054144859313965 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q141488 |
| concepts[4].display_name | Graph |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4791766405105591 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C80444323 |
| concepts[6].level | 1 |
| concepts[6].score | 0.43918001651763916 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[6].display_name | Theoretical computer science |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.41926777362823486 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C2780980858 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4131987392902374 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q110022 |
| concepts[8].display_name | Dual (grammatical number) |
| concepts[9].id | https://openalex.org/C86803240 |
| concepts[9].level | 0 |
| concepts[9].score | 0.1456216275691986 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[9].display_name | Biology |
| concepts[10].id | https://openalex.org/C67705224 |
| concepts[10].level | 3 |
| concepts[10].score | 0.12966325879096985 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11053 |
| concepts[10].display_name | RNA |
| concepts[11].id | https://openalex.org/C104317684 |
| concepts[11].level | 2 |
| concepts[11].score | 0.08355137705802917 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[11].display_name | Gene |
| concepts[12].id | https://openalex.org/C124952713 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8242 |
| concepts[12].display_name | Literature |
| concepts[13].id | https://openalex.org/C142362112 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q735 |
| concepts[13].display_name | Art |
| concepts[14].id | https://openalex.org/C55493867 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[14].display_name | Biochemistry |
| keywords[0].id | https://openalex.org/keywords/pairwise-comparison |
| keywords[0].score | 0.7483431100845337 |
| keywords[0].display_name | Pairwise comparison |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6460825800895691 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/bipartite-graph |
| keywords[2].score | 0.5199650526046753 |
| keywords[2].display_name | Bipartite graph |
| keywords[3].id | https://openalex.org/keywords/non-coding-rna |
| keywords[3].score | 0.5147395133972168 |
| keywords[3].display_name | Non-coding RNA |
| keywords[4].id | https://openalex.org/keywords/graph |
| keywords[4].score | 0.5054144859313965 |
| keywords[4].display_name | Graph |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4791766405105591 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[6].score | 0.43918001651763916 |
| keywords[6].display_name | Theoretical computer science |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.41926777362823486 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/dual |
| keywords[8].score | 0.4131987392902374 |
| keywords[8].display_name | Dual (grammatical number) |
| keywords[9].id | https://openalex.org/keywords/biology |
| keywords[9].score | 0.1456216275691986 |
| keywords[9].display_name | Biology |
| keywords[10].id | https://openalex.org/keywords/rna |
| keywords[10].score | 0.12966325879096985 |
| keywords[10].display_name | RNA |
| keywords[11].id | https://openalex.org/keywords/gene |
| keywords[11].score | 0.08355137705802917 |
| keywords[11].display_name | Gene |
| language | en |
| locations[0].id | doi:10.1093/bib/bbac339 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S91767247 |
| locations[0].source.issn | 1467-5463, 1477-4054 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1467-5463 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Briefings in Bioinformatics |
| locations[0].source.host_organization | https://openalex.org/P4310311648 |
| locations[0].source.host_organization_name | Oxford University Press |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| locations[0].source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| locations[0].license | |
| locations[0].pdf_url | https://academic.oup.com/bib/article-pdf/23/6/bbac339/48076007/bbac339.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Briefings in Bioinformatics |
| locations[0].landing_page_url | https://doi.org/10.1093/bib/bbac339 |
| locations[1].id | pmid:36063562 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Briefings in bioinformatics |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/36063562 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5004683765 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6586-0533 |
| authorships[0].author.display_name | Linlin Zhuo |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I146620803, https://openalex.org/I4400573270 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Data Science and Artificial Intelligence, Wenzhou University of Technology , 325027, Wenzhou , China |
| authorships[0].institutions[0].id | https://openalex.org/I4400573270 |
| authorships[0].institutions[0].ror | https://ror.org/03dd7qj98 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4400573270 |
| authorships[0].institutions[0].country_code | |
| authorships[0].institutions[0].display_name | Wenzhou University of Technology |
| authorships[0].institutions[1].id | https://openalex.org/I146620803 |
| authorships[0].institutions[1].ror | https://ror.org/020hxh324 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I146620803 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Wenzhou University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Linlin Zhuo |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Data Science and Artificial Intelligence, Wenzhou University of Technology , 325027, Wenzhou , China |
| authorships[1].author.id | https://openalex.org/A5034581482 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1479-5399 |
| authorships[1].author.display_name | Bosheng Song |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I16609230 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Computer Science and Electronic Engineering, Hunan University , 410082, Changsha , China |
| authorships[1].institutions[0].id | https://openalex.org/I16609230 |
| authorships[1].institutions[0].ror | https://ror.org/05htk5m33 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I16609230 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Hunan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | bosheng song |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | College of Computer Science and Electronic Engineering, Hunan University , 410082, Changsha , China |
| authorships[2].author.id | https://openalex.org/A5027387223 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | yuansheng liu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I16609230 |
| authorships[2].affiliations[0].raw_affiliation_string | College of Computer Science and Electronic Engineering, Hunan University , 410082, Changsha , China |
| authorships[2].institutions[0].id | https://openalex.org/I16609230 |
| authorships[2].institutions[0].ror | https://ror.org/05htk5m33 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I16609230 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Hunan University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | yuansheng liu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | College of Computer Science and Electronic Engineering, Hunan University , 410082, Changsha , China |
| authorships[3].author.id | https://openalex.org/A5101725945 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8751-9387 |
| authorships[3].author.display_name | Zejun Li |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I96478251 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Computer and Information Science, Hunan Institute of Technology , 421000, Hengyang , China |
| authorships[3].institutions[0].id | https://openalex.org/I96478251 |
| authorships[3].institutions[0].ror | https://ror.org/04n3k2k71 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I96478251 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Hunan Institute of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zejun Li |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Computer and Information Science, Hunan Institute of Technology , 421000, Hengyang , China |
| authorships[4].author.id | https://openalex.org/A5044283271 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6840-2573 |
| authorships[4].author.display_name | Xiangzheng Fu |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I16609230 |
| authorships[4].affiliations[0].raw_affiliation_string | College of Computer Science and Electronic Engineering, Hunan University , 410082, Changsha , China |
| authorships[4].institutions[0].id | https://openalex.org/I16609230 |
| authorships[4].institutions[0].ror | https://ror.org/05htk5m33 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I16609230 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Hunan University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Xiangzheng Fu |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | College of Computer Science and Electronic Engineering, Hunan University , 410082, Changsha , China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://academic.oup.com/bib/article-pdf/23/6/bbac339/48076007/bbac339.pdf |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10515 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1306 |
| primary_topic.subfield.display_name | Cancer Research |
| primary_topic.display_name | Cancer-related molecular mechanisms research |
| related_works | https://openalex.org/W2371352078, https://openalex.org/W2487162673, https://openalex.org/W2942366970, https://openalex.org/W2793211469, https://openalex.org/W2077383796, https://openalex.org/W2949152769, https://openalex.org/W2953461625, https://openalex.org/W4372354731, https://openalex.org/W1692008701, https://openalex.org/W2597588799 |
| cited_by_count | 18 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 7 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 7 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1093/bib/bbac339 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S91767247 |
| best_oa_location.source.issn | 1467-5463, 1477-4054 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1467-5463 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Briefings in Bioinformatics |
| best_oa_location.source.host_organization | https://openalex.org/P4310311648 |
| best_oa_location.source.host_organization_name | Oxford University Press |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| best_oa_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://academic.oup.com/bib/article-pdf/23/6/bbac339/48076007/bbac339.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Briefings in Bioinformatics |
| best_oa_location.landing_page_url | https://doi.org/10.1093/bib/bbac339 |
| primary_location.id | doi:10.1093/bib/bbac339 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S91767247 |
| primary_location.source.issn | 1467-5463, 1477-4054 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1467-5463 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Briefings in Bioinformatics |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| primary_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| primary_location.license | |
| primary_location.pdf_url | https://academic.oup.com/bib/article-pdf/23/6/bbac339/48076007/bbac339.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Briefings in Bioinformatics |
| primary_location.landing_page_url | https://doi.org/10.1093/bib/bbac339 |
| publication_date | 2022-08-31 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2024628274, https://openalex.org/W2010691380, https://openalex.org/W1979906856, https://openalex.org/W2006751864, https://openalex.org/W4214837029, https://openalex.org/W3205135608, https://openalex.org/W2158898099, https://openalex.org/W2148542126, https://openalex.org/W2050002679, https://openalex.org/W2076563297, https://openalex.org/W6712288771, https://openalex.org/W2070777085, https://openalex.org/W2039816251, https://openalex.org/W2053003053, https://openalex.org/W1978883675, https://openalex.org/W2048085228, https://openalex.org/W2044117548, https://openalex.org/W1982267716, https://openalex.org/W2103683291, https://openalex.org/W2072394638, https://openalex.org/W3112802026, https://openalex.org/W3002341417, https://openalex.org/W2081468394, https://openalex.org/W2752850911, https://openalex.org/W2904828342, https://openalex.org/W3144812654, https://openalex.org/W2025150647, https://openalex.org/W2119501676, https://openalex.org/W6692429512, https://openalex.org/W6790982925, https://openalex.org/W2507804718, https://openalex.org/W2807607992, https://openalex.org/W2971428843, https://openalex.org/W4206452051, https://openalex.org/W6891937993, https://openalex.org/W2990026901, https://openalex.org/W2198606573, https://openalex.org/W3094094527, https://openalex.org/W2953664435, https://openalex.org/W2920083709, https://openalex.org/W3125850677, https://openalex.org/W2951050019, https://openalex.org/W3024028653, https://openalex.org/W3113349968, https://openalex.org/W2108051165, https://openalex.org/W4212920271, https://openalex.org/W2898598946, https://openalex.org/W2064515831, https://openalex.org/W6767796974, https://openalex.org/W4212774754, https://openalex.org/W1583837637, https://openalex.org/W4250394585, https://openalex.org/W3097982973, https://openalex.org/W2975753138, https://openalex.org/W2612690371, https://openalex.org/W2256799280, https://openalex.org/W3131891239, https://openalex.org/W2397250075 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 116, 140, 145, 155 |
| abstract_inverted_index.At | 203 |
| abstract_inverted_index.In | 111 |
| abstract_inverted_index.To | 130 |
| abstract_inverted_index.We | 185 |
| abstract_inverted_index.an | 163 |
| abstract_inverted_index.as | 33 |
| abstract_inverted_index.at | 242 |
| abstract_inverted_index.by | 61 |
| abstract_inverted_index.in | 14, 76 |
| abstract_inverted_index.is | 20, 135, 169, 240 |
| abstract_inverted_index.of | 150, 162 |
| abstract_inverted_index.on | 82, 88, 99, 108, 122, 175, 196, 221 |
| abstract_inverted_index.or | 65 |
| abstract_inverted_index.to | 10, 23, 97, 138, 158, 172, 198, 215 |
| abstract_inverted_index.we | 114, 153, 207 |
| abstract_inverted_index.And | 237 |
| abstract_inverted_index.NPI | 85, 106, 119 |
| abstract_inverted_index.The | 16 |
| abstract_inverted_index.and | 49, 53, 79 |
| abstract_inverted_index.are | 46 |
| abstract_inverted_index.but | 45, 102 |
| abstract_inverted_index.can | 41 |
| abstract_inverted_index.due | 9 |
| abstract_inverted_index.for | 105 |
| abstract_inverted_index.key | 12 |
| abstract_inverted_index.may | 30 |
| abstract_inverted_index.not | 103 |
| abstract_inverted_index.our | 131, 133, 167, 228 |
| abstract_inverted_index.the | 74, 136, 160, 170, 176, 182, 187, 204, 222, 238 |
| abstract_inverted_index.two | 193, 212 |
| abstract_inverted_index.use | 154 |
| abstract_inverted_index.GNN, | 89 |
| abstract_inverted_index.NPIs | 174 |
| abstract_inverted_index.RNAs | 2 |
| abstract_inverted_index.been | 95 |
| abstract_inverted_index.deep | 54 |
| abstract_inverted_index.dual | 123 |
| abstract_inverted_index.etc. | 37 |
| abstract_inverted_index.four | 223 |
| abstract_inverted_index.have | 4, 57, 94 |
| abstract_inverted_index.into | 192 |
| abstract_inverted_index.like | 84 |
| abstract_inverted_index.rank | 156 |
| abstract_inverted_index.same | 205 |
| abstract_inverted_index.some | 25, 90 |
| abstract_inverted_index.such | 32 |
| abstract_inverted_index.than | 181 |
| abstract_inverted_index.that | 28, 227 |
| abstract_inverted_index.this | 43, 112 |
| abstract_inverted_index.used | 104 |
| abstract_inverted_index.well | 81 |
| abstract_inverted_index.with | 233 |
| abstract_inverted_index.work | 44 |
| abstract_inverted_index.(GCN) | 127 |
| abstract_inverted_index.(NPI) | 19 |
| abstract_inverted_index.Based | 87 |
| abstract_inverted_index.Graph | 68, 124 |
| abstract_inverted_index.apply | 98 |
| abstract_inverted_index.based | 121 |
| abstract_inverted_index.first | 137, 171 |
| abstract_inverted_index.graph | 179, 191 |
| abstract_inverted_index.great | 59 |
| abstract_inverted_index.layer | 157 |
| abstract_inverted_index.model | 143, 168, 208, 239 |
| abstract_inverted_index.ncRNA | 29 |
| abstract_inverted_index.often | 21, 47 |
| abstract_inverted_index.other | 234 |
| abstract_inverted_index.pair. | 165 |
| abstract_inverted_index.roles | 13 |
| abstract_inverted_index.score | 161 |
| abstract_inverted_index.tasks | 83 |
| abstract_inverted_index.their | 11 |
| abstract_inverted_index.time, | 206 |
| abstract_inverted_index.train | 139 |
| abstract_inverted_index.using | 144 |
| abstract_inverted_index.which | 197 |
| abstract_inverted_index.Neural | 69 |
| abstract_inverted_index.binary | 151 |
| abstract_inverted_index.called | 128 |
| abstract_inverted_index.direct | 209 |
| abstract_inverted_index.graph. | 184 |
| abstract_inverted_index.graphs | 78, 195, 214 |
| abstract_inverted_index.method | 134, 229 |
| abstract_inverted_index.paper, | 113 |
| abstract_inverted_index.rather | 180 |
| abstract_inverted_index.reveal | 24 |
| abstract_inverted_index.Instead | 149 |
| abstract_inverted_index.Machine | 51 |
| abstract_inverted_index.Network | 70, 126 |
| abstract_inverted_index.affect, | 31 |
| abstract_inverted_index.between | 211 |
| abstract_inverted_index.explore | 199, 216 |
| abstract_inverted_index.methods | 40, 56, 72, 93 |
| abstract_inverted_index.perform | 80 |
| abstract_inverted_index.predict | 173 |
| abstract_inverted_index.results | 220 |
| abstract_inverted_index.success | 60 |
| abstract_inverted_index.traits, | 35 |
| abstract_inverted_index.(ncRNAs) | 3 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.achieved | 58 |
| abstract_inverted_index.achieves | 230 |
| abstract_inverted_index.biology. | 15 |
| abstract_inverted_index.consider | 73 |
| abstract_inverted_index.datasets | 225 |
| abstract_inverted_index.explicit | 217 |
| abstract_inverted_index.explored | 22 |
| abstract_inverted_index.implicit | 201 |
| abstract_inverted_index.indicate | 226 |
| abstract_inverted_index.learning | 52, 55, 147 |
| abstract_inverted_index.methods. | 236 |
| abstract_inverted_index.original | 188 |
| abstract_inverted_index.pairwise | 91, 117, 146 |
| abstract_inverted_index.recently | 5 |
| abstract_inverted_index.sequence | 64 |
| abstract_inverted_index.standard | 224 |
| abstract_inverted_index.topology | 75 |
| abstract_inverted_index.Moreover, | 166 |
| abstract_inverted_index.NPI-DGCN. | 129 |
| abstract_inverted_index.Noncoding | 1 |
| abstract_inverted_index.attention | 8 |
| abstract_inverted_index.attracted | 6 |
| abstract_inverted_index.available | 241 |
| abstract_inverted_index.bipartite | 178, 190 |
| abstract_inverted_index.calculate | 159 |
| abstract_inverted_index.construct | 115 |
| abstract_inverted_index.developed | 96 |
| abstract_inverted_index.diseases, | 36 |
| abstract_inverted_index.networks, | 101 |
| abstract_inverted_index.networks. | 110 |
| abstract_inverted_index.predictor | 120 |
| abstract_inverted_index.strategy. | 148 |
| abstract_inverted_index.structure | 66 |
| abstract_inverted_index.transform | 186 |
| abstract_inverted_index.accomplish | 42 |
| abstract_inverted_index.activities | 27 |
| abstract_inverted_index.biological | 26, 34 |
| abstract_inverted_index.constraint | 92 |
| abstract_inverted_index.expensive. | 50 |
| abstract_inverted_index.exploiting | 62 |
| abstract_inverted_index.homogenous | 194, 213 |
| abstract_inverted_index.knowledge, | 132 |
| abstract_inverted_index.prediction | 107 |
| abstract_inverted_index.sufficient | 63 |
| abstract_inverted_index.(GNN)-based | 71 |
| abstract_inverted_index.Traditional | 38 |
| abstract_inverted_index.competitive | 231 |
| abstract_inverted_index.constrained | 118 |
| abstract_inverted_index.graph-based | 142 |
| abstract_inverted_index.homogeneous | 100, 183 |
| abstract_inverted_index.interaction | 18 |
| abstract_inverted_index.performance | 232 |
| abstract_inverted_index.prediction. | 86 |
| abstract_inverted_index.Experimental | 219 |
| abstract_inverted_index.considerable | 7 |
| abstract_inverted_index.experimental | 39 |
| abstract_inverted_index.information. | 67 |
| abstract_inverted_index.interactions | 210 |
| abstract_inverted_index.second-order | 200 |
| abstract_inverted_index.Convolutional | 125 |
| abstract_inverted_index.heterogeneous | 109, 141 |
| abstract_inverted_index.relationships. | 202, 218 |
| abstract_inverted_index.classification, | 152 |
| abstract_inverted_index.labor-intensive | 48 |
| abstract_inverted_index.ncRNA–protein | 77, 164, 177, 189 |
| abstract_inverted_index.ncRNA–proteins | 17 |
| abstract_inverted_index.state-of-the-art | 235 |
| abstract_inverted_index.https://github.com/zhuoninnin1992/NPIPredict | 243 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5044283271 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I16609230 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.86707299 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |