UNICORN: Towards universal cellular expression prediction with a multi-task learning framework Article Swipe
Tianyu Liu
,
Tinglin Huang
,
Lijun Wang
,
Yingxin Lin
,
Rex Ying
,
Hongyu Zhao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-64506-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-64506-8
Sequence-to-function analysis is a challenging task in human genetics, especially in predicting cell-type-specific multi-omic phenotypes from biological sequences such as individualized gene expression. Here, we present UNICORN, a computational method with improved prediction performances than the existing methods. UNICORN takes the embeddings from biological sequences as well as external knowledge from pre-trained foundation models as inputs and optimizes the predictor with carefully-designed loss functions. We demonstrate that UNICORN outperforms the existing methods in both gene expression prediction and multi-omic phenotype prediction at the cellular level and the cell-type level, and it can also generate uncertainty scores of the predictions. Moreover, UNICORN is able to link personalized gene expression profiles with corresponding genome information. Finally, we show that UNICORN is capable of characterizing complex biological systems for different disease states or perturbations. Overall, embeddings from foundation models can facilitate the understanding of the role of biological sequences in the prediction task, and incorporating multi-omic information can enhance prediction performances.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-025-64506-8
- https://www.nature.com/articles/s41467-025-64506-8.pdf
- OA Status
- gold
- References
- 74
- OpenAlex ID
- https://openalex.org/W4415585927
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415585927Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41467-025-64506-8Digital Object Identifier
- Title
-
UNICORN: Towards universal cellular expression prediction with a multi-task learning frameworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-27Full publication date if available
- Authors
-
Tianyu Liu, Tinglin Huang, Lijun Wang, Yingxin Lin, Rex Ying, Hongyu ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1038/s41467-025-64506-8Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41467-025-64506-8.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41467-025-64506-8.pdfDirect OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
74Number of works referenced by this work
Full payload
| id | https://openalex.org/W4415585927 |
|---|---|
| doi | https://doi.org/10.1038/s41467-025-64506-8 |
| ids.doi | https://doi.org/10.1038/s41467-025-64506-8 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/41145510 |
| ids.openalex | https://openalex.org/W4415585927 |
| fwci | 0.0 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | Q000379 |
| mesh[1].descriptor_ui | D019295 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | methods |
| mesh[1].descriptor_name | Computational Biology |
| mesh[2].qualifier_ui | Q000379 |
| mesh[2].descriptor_ui | D020869 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | methods |
| mesh[2].descriptor_name | Gene Expression Profiling |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D000465 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Algorithms |
| 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 | D059467 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Transcriptome |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D010641 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Phenotype |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D006801 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Humans |
| mesh[8].qualifier_ui | Q000379 |
| mesh[8].descriptor_ui | D019295 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | methods |
| mesh[8].descriptor_name | Computational Biology |
| mesh[9].qualifier_ui | Q000379 |
| mesh[9].descriptor_ui | D020869 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | methods |
| mesh[9].descriptor_name | Gene Expression Profiling |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D000465 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Algorithms |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D000069550 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Machine Learning |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D059467 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Transcriptome |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D010641 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Phenotype |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D006801 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Humans |
| mesh[15].qualifier_ui | Q000379 |
| mesh[15].descriptor_ui | D019295 |
| mesh[15].is_major_topic | True |
| mesh[15].qualifier_name | methods |
| mesh[15].descriptor_name | Computational Biology |
| mesh[16].qualifier_ui | Q000379 |
| mesh[16].descriptor_ui | D020869 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | methods |
| mesh[16].descriptor_name | Gene Expression Profiling |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D000465 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Algorithms |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D000069550 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Machine Learning |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D059467 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Transcriptome |
| mesh[20].qualifier_ui | |
| mesh[20].descriptor_ui | D010641 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | |
| mesh[20].descriptor_name | Phenotype |
| type | article |
| title | UNICORN: Towards universal cellular expression prediction with a multi-task learning framework |
| biblio.issue | 1 |
| biblio.volume | 16 |
| biblio.last_page | 9455 |
| biblio.first_page | 9455 |
| topics[0].id | https://openalex.org/T11289 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1312 |
| topics[0].subfield.display_name | Molecular Biology |
| topics[0].display_name | Single-cell and spatial transcriptomics |
| topics[1].id | https://openalex.org/T12859 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9993000030517578 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1304 |
| topics[1].subfield.display_name | Biophysics |
| topics[1].display_name | Cell Image Analysis Techniques |
| topics[2].id | https://openalex.org/T10885 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9858999848365784 |
| 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 | Gene expression and cancer classification |
| is_xpac | False |
| apc_list.value | 3920 |
| apc_list.currency | GBP |
| apc_list.value_usd | 4808 |
| apc_paid.value | 3920 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 4808 |
| language | en |
| locations[0].id | doi:10.1038/s41467-025-64506-8 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S64187185 |
| locations[0].source.issn | 2041-1723 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2041-1723 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Nature Communications |
| locations[0].source.host_organization | https://openalex.org/P4310319908 |
| locations[0].source.host_organization_name | Nature Portfolio |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://www.nature.com/articles/s41467-025-64506-8.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Nature Communications |
| locations[0].landing_page_url | https://doi.org/10.1038/s41467-025-64506-8 |
| locations[1].id | pmid:41145510 |
| 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 | Nature communications |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/41145510 |
| locations[2].id | pmh:oai:doaj.org/article:c82987fc0d92453f892061cc7ec0a2ac |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Nature Communications, Vol 16, Iss 1, Pp 1-14 (2025) |
| locations[2].landing_page_url | https://doaj.org/article/c82987fc0d92453f892061cc7ec0a2ac |
| locations[3].id | pmh:oai:europepmc.org:11368653 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400806 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | Europe PMC (PubMed Central) |
| locations[3].source.host_organization | https://openalex.org/I1303153112 |
| locations[3].source.host_organization_name | European Bioinformatics Institute |
| locations[3].source.host_organization_lineage | https://openalex.org/I1303153112 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12559380 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5017004315 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9412-6573 |
| authorships[0].author.display_name | Tianyu Liu |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I32971472 |
| authorships[0].affiliations[0].raw_affiliation_string | Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA |
| authorships[0].institutions[0].id | https://openalex.org/I32971472 |
| authorships[0].institutions[0].ror | https://ror.org/03v76x132 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I32971472 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Yale University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Tianyu Liu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA |
| authorships[1].author.id | https://openalex.org/A5101942668 |
| authorships[1].author.orcid | https://orcid.org/0009-0005-5644-4879 |
| authorships[1].author.display_name | Tinglin Huang |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I32971472 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, Yale University, New Haven, CT, USA |
| authorships[1].institutions[0].id | https://openalex.org/I32971472 |
| authorships[1].institutions[0].ror | https://ror.org/03v76x132 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I32971472 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Yale University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Tinglin Huang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, Yale University, New Haven, CT, USA |
| authorships[2].author.id | https://openalex.org/A5075705200 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5222-4506 |
| authorships[2].author.display_name | Lijun Wang |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I32971472 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Biostatistics, Yale University, New Haven, CT, USA |
| authorships[2].institutions[0].id | https://openalex.org/I32971472 |
| authorships[2].institutions[0].ror | https://ror.org/03v76x132 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I32971472 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Yale University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Lijun Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Biostatistics, Yale University, New Haven, CT, USA |
| authorships[3].author.id | https://openalex.org/A5033771420 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4299-7326 |
| authorships[3].author.display_name | Yingxin Lin |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I32971472 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Biostatistics, Yale University, New Haven, CT, USA |
| authorships[3].institutions[0].id | https://openalex.org/I32971472 |
| authorships[3].institutions[0].ror | https://ror.org/03v76x132 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I32971472 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Yale University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yingxin Lin |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Biostatistics, Yale University, New Haven, CT, USA |
| authorships[4].author.id | https://openalex.org/A5078337825 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5856-5229 |
| authorships[4].author.display_name | Rex Ying |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I32971472 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer Science, Yale University, New Haven, CT, USA |
| authorships[4].institutions[0].id | https://openalex.org/I32971472 |
| authorships[4].institutions[0].ror | https://ror.org/03v76x132 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I32971472 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Yale University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Rex Ying |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Computer Science, Yale University, New Haven, CT, USA |
| authorships[5].author.id | https://openalex.org/A5074828414 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-1195-9607 |
| authorships[5].author.display_name | Hongyu Zhao |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I32971472 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Biostatistics, Yale University, New Haven, CT, USA |
| authorships[5].institutions[0].id | https://openalex.org/I32971472 |
| authorships[5].institutions[0].ror | https://ror.org/03v76x132 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I32971472 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Yale University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Hongyu Zhao |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Biostatistics, Yale University, New Haven, CT, USA |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.nature.com/articles/s41467-025-64506-8.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-28T00:00:00 |
| display_name | UNICORN: Towards universal cellular expression prediction with a multi-task learning framework |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11289 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1312 |
| primary_topic.subfield.display_name | Molecular Biology |
| primary_topic.display_name | Single-cell and spatial transcriptomics |
| cited_by_count | 0 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1038/s41467-025-64506-8 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S64187185 |
| best_oa_location.source.issn | 2041-1723 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2041-1723 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Nature Communications |
| best_oa_location.source.host_organization | https://openalex.org/P4310319908 |
| best_oa_location.source.host_organization_name | Nature Portfolio |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://www.nature.com/articles/s41467-025-64506-8.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Nature Communications |
| best_oa_location.landing_page_url | https://doi.org/10.1038/s41467-025-64506-8 |
| primary_location.id | doi:10.1038/s41467-025-64506-8 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S64187185 |
| primary_location.source.issn | 2041-1723 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2041-1723 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Nature Communications |
| primary_location.source.host_organization | https://openalex.org/P4310319908 |
| primary_location.source.host_organization_name | Nature Portfolio |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://www.nature.com/articles/s41467-025-64506-8.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Nature Communications |
| primary_location.landing_page_url | https://doi.org/10.1038/s41467-025-64506-8 |
| publication_date | 2025-10-27 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W1957469265, https://openalex.org/W2273910198, https://openalex.org/W2198606573, https://openalex.org/W2883945062, https://openalex.org/W4401933237, https://openalex.org/W4401063335, https://openalex.org/W2073727170, https://openalex.org/W2157585454, https://openalex.org/W4396721167, https://openalex.org/W4385333137, https://openalex.org/W3013151148, https://openalex.org/W2069295116, https://openalex.org/W4223485171, https://openalex.org/W1602702667, https://openalex.org/W2739492614, https://openalex.org/W2025828346, https://openalex.org/W4372333585, https://openalex.org/W4280594254, https://openalex.org/W4404732658, https://openalex.org/W3203588026, https://openalex.org/W4403387533, https://openalex.org/W2952239877, https://openalex.org/W4406178430, https://openalex.org/W2746479894, https://openalex.org/W4305014070, https://openalex.org/W4402720510, https://openalex.org/W3114632476, https://openalex.org/W3127238141, https://openalex.org/W4404349982, https://openalex.org/W3166142427, https://openalex.org/W4362515116, https://openalex.org/W2092913562, https://openalex.org/W4404821554, https://openalex.org/W2952631022, https://openalex.org/W4405114593, https://openalex.org/W4385838588, https://openalex.org/W2336509392, https://openalex.org/W4406155921, https://openalex.org/W4223893712, https://openalex.org/W2809330972, https://openalex.org/W3133082627, https://openalex.org/W3210860076, https://openalex.org/W4366085876, https://openalex.org/W2901677030, https://openalex.org/W2980495984, https://openalex.org/W2995735132, https://openalex.org/W2103017472, https://openalex.org/W4323036511, https://openalex.org/W3084033366, https://openalex.org/W2897748644, https://openalex.org/W2178888844, https://openalex.org/W220770808, https://openalex.org/W3133859624, https://openalex.org/W4389174422, https://openalex.org/W4389174423, https://openalex.org/W2806225217, https://openalex.org/W4200473178, https://openalex.org/W3090295751, https://openalex.org/W4283257569, https://openalex.org/W4387569894, https://openalex.org/W3035339992, https://openalex.org/W4327550249, https://openalex.org/W4315621825, https://openalex.org/W4290716979, https://openalex.org/W4313648826, https://openalex.org/W4376643505, https://openalex.org/W2753709519, https://openalex.org/W4225278475, https://openalex.org/W4378838672, https://openalex.org/W4400294055, https://openalex.org/W4407360292, https://openalex.org/W4402660143, https://openalex.org/W2800392236, https://openalex.org/W6893554069 |
| referenced_works_count | 74 |
| abstract_inverted_index.a | 3, 27 |
| abstract_inverted_index.We | 64 |
| abstract_inverted_index.as | 19, 45, 47, 54 |
| abstract_inverted_index.at | 81 |
| abstract_inverted_index.in | 6, 10, 72, 146 |
| abstract_inverted_index.is | 2, 101, 118 |
| abstract_inverted_index.it | 90 |
| abstract_inverted_index.of | 96, 120, 140, 143 |
| abstract_inverted_index.or | 129 |
| abstract_inverted_index.to | 103 |
| abstract_inverted_index.we | 24, 114 |
| abstract_inverted_index.and | 56, 77, 85, 89, 150 |
| abstract_inverted_index.can | 91, 136, 154 |
| abstract_inverted_index.for | 125 |
| abstract_inverted_index.the | 35, 40, 58, 69, 82, 86, 97, 138, 141, 147 |
| abstract_inverted_index.able | 102 |
| abstract_inverted_index.also | 92 |
| abstract_inverted_index.both | 73 |
| abstract_inverted_index.from | 15, 42, 50, 133 |
| abstract_inverted_index.gene | 21, 74, 106 |
| abstract_inverted_index.link | 104 |
| abstract_inverted_index.loss | 62 |
| abstract_inverted_index.role | 142 |
| abstract_inverted_index.show | 115 |
| abstract_inverted_index.such | 18 |
| abstract_inverted_index.task | 5 |
| abstract_inverted_index.than | 34 |
| abstract_inverted_index.that | 66, 116 |
| abstract_inverted_index.well | 46 |
| abstract_inverted_index.with | 30, 60, 109 |
| abstract_inverted_index.Here, | 23 |
| abstract_inverted_index.human | 7 |
| abstract_inverted_index.level | 84 |
| abstract_inverted_index.takes | 39 |
| abstract_inverted_index.task, | 149 |
| abstract_inverted_index.genome | 111 |
| abstract_inverted_index.inputs | 55 |
| abstract_inverted_index.level, | 88 |
| abstract_inverted_index.method | 29 |
| abstract_inverted_index.models | 53, 135 |
| abstract_inverted_index.scores | 95 |
| abstract_inverted_index.states | 128 |
| abstract_inverted_index.UNICORN | 38, 67, 100, 117 |
| abstract_inverted_index.capable | 119 |
| abstract_inverted_index.complex | 122 |
| abstract_inverted_index.disease | 127 |
| abstract_inverted_index.enhance | 155 |
| abstract_inverted_index.methods | 71 |
| abstract_inverted_index.present | 25 |
| abstract_inverted_index.systems | 124 |
| abstract_inverted_index.Finally, | 113 |
| abstract_inverted_index.Overall, | 131 |
| abstract_inverted_index.UNICORN, | 26 |
| abstract_inverted_index.analysis | 1 |
| abstract_inverted_index.cellular | 83 |
| abstract_inverted_index.existing | 36, 70 |
| abstract_inverted_index.external | 48 |
| abstract_inverted_index.generate | 93 |
| abstract_inverted_index.improved | 31 |
| abstract_inverted_index.methods. | 37 |
| abstract_inverted_index.profiles | 108 |
| abstract_inverted_index.Moreover, | 99 |
| abstract_inverted_index.cell-type | 87 |
| abstract_inverted_index.different | 126 |
| abstract_inverted_index.genetics, | 8 |
| abstract_inverted_index.knowledge | 49 |
| abstract_inverted_index.optimizes | 57 |
| abstract_inverted_index.phenotype | 79 |
| abstract_inverted_index.predictor | 59 |
| abstract_inverted_index.sequences | 17, 44, 145 |
| abstract_inverted_index.biological | 16, 43, 123, 144 |
| abstract_inverted_index.embeddings | 41, 132 |
| abstract_inverted_index.especially | 9 |
| abstract_inverted_index.expression | 75, 107 |
| abstract_inverted_index.facilitate | 137 |
| abstract_inverted_index.foundation | 52, 134 |
| abstract_inverted_index.functions. | 63 |
| abstract_inverted_index.multi-omic | 13, 78, 152 |
| abstract_inverted_index.phenotypes | 14 |
| abstract_inverted_index.predicting | 11 |
| abstract_inverted_index.prediction | 32, 76, 80, 148, 156 |
| abstract_inverted_index.challenging | 4 |
| abstract_inverted_index.demonstrate | 65 |
| abstract_inverted_index.expression. | 22 |
| abstract_inverted_index.information | 153 |
| abstract_inverted_index.outperforms | 68 |
| abstract_inverted_index.pre-trained | 51 |
| abstract_inverted_index.uncertainty | 94 |
| abstract_inverted_index.information. | 112 |
| abstract_inverted_index.performances | 33 |
| abstract_inverted_index.personalized | 105 |
| abstract_inverted_index.predictions. | 98 |
| abstract_inverted_index.computational | 28 |
| abstract_inverted_index.corresponding | 110 |
| abstract_inverted_index.incorporating | 151 |
| abstract_inverted_index.performances. | 157 |
| abstract_inverted_index.understanding | 139 |
| abstract_inverted_index.characterizing | 121 |
| abstract_inverted_index.individualized | 20 |
| abstract_inverted_index.perturbations. | 130 |
| abstract_inverted_index.carefully-designed | 61 |
| abstract_inverted_index.cell-type-specific | 12 |
| abstract_inverted_index.Sequence-to-function | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.48893471 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |