{"doi":"10.1186/s13073-021-00909-8","title":"PhenCards: a data resource linking human phenotype information to biomedical knowledge","abstract":"<jats:title>Abstract</jats:title><jats:p>We present PhenCards (<jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://phencards.org/\">https://phencards.org</jats:ext-link>), a database and web server intended as a one-stop shop for previously disconnected biomedical knowledge related to human clinical phenotypes. Users can query human phenotype terms or clinical notes. PhenCards obtains relevant disease/phenotype prevalence and co-occurrence, drug, procedural, pathway, literature, grant, and collaborator data. PhenCards recommends the most probable genetic diseases and candidate genes based on phenotype terms from clinical notes. PhenCards facilitates exploration of phenotype, e.g., which drugs cause or are prescribed for patient symptoms, which genes likely cause specific symptoms, and which comorbidities co-occur with phenotypes.</jats:p>","journal":"Genome Medicine","year":2021,"id":14096,"datarank":0.44989104229073923,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.12030735569030626,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.12030735569030626,"corpus_percentile":50.8,"corpus_rank":614,"citation_count":8,"citer_count":5,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":2,"is_dataset":true,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":57.2917,"fair_percentile":91.73262972735269,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":84629,"name":"Cong Liu","orcid":"0000-0001-6024-3037","position":1,"is_corresponding":false},{"id":112868,"name":"Xiangchen Dong","orcid":null,"position":2,"is_corresponding":false},{"id":2012,"name":"Chunhua Weng","orcid":"0000-0002-9624-0214","position":3,"is_corresponding":false},{"id":109734,"name":"Kai Wang","orcid":"0000-0002-4991-9259","position":4,"is_corresponding":false},{"id":112867,"name":"James M. Havrilla","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"34034817","pmcid":"PMC8147460","openalex_id":"https://openalex.org/W3164771895","authors":[],"funders":[{"funder_name":"U.S. National Library of Medicine","grant_id":"R01 LM012895","title":null},{"funder_name":"National Institute of General Medical Sciences","grant_id":"R01 GM132713","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01LM012895-03","title":"Deep phenotyping in Electronic Health Records for Genomic Medicine"},{"funder_name":"National Institutes of Health","grant_id":"5R01GM132713-04","title":"Detection and annotation of structural variants from long-read sequencing"}],"total_grants":4,"fwci":0.6513,"citation_percentile":0.66580334,"influential_citations":0,"citation_trend":[{"year":2021,"count":2},{"year":2022,"count":2},{"year":2023,"count":1},{"year":2024,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://genomemedicine.biomedcentral.com/track/pdf/10.1186/s13073-021-00909-8","host_type":"journal"},{"url":"https://genomemedicine.biomedcentral.com/track/pdf/10.1186/s13073-021-00909-8","host_type":"GOLD"},{"url":"https://genomemedicine.biomedcentral.com/track/pdf/10.1186/s13073-021-00909-8","host_type":"publisher"},{"url":"https://link.springer.com/content/pdf/10.1186/s13073-021-00909-8.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1186/s13073-021-00909-8/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1186/s13073-021-00909-8","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/34034817","host_type":"repository"},{"url":"https://doaj.org/article/4ced8467f0ff485ea5ab62c9d5403a14","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8147460","host_type":"repository"},{"url":"https://doi.org/10.7916/gpja-fq66","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC8147460","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC8147460?pdf=render","host_type":"Europe_PMC"},{"url":"https://dx.doi.org/10.7916/gpja-fq66","host_type":""},{"url":"http://dx.doi.org/10.1186/s13073-021-00909-8","host_type":""},{"url":"https://dx.doi.org/10.1186/s13073-021-00909-8","host_type":""},{"url":"https://doi.org/https://doi.org/10.7916/gpja-fq66","host_type":""}],"fields_of_study":["Biomedical Text Mining and Ontologies","Genomics and Rare Diseases","Bioinformatics and Genomic Networks","Medicine","Computer Science","Computational Biology","Databases, Factual","Disease Susceptibility","Genetic Association Studies","Genetic Predisposition to Disease","Humans","Phenotype","Web Browser","Workflow"],"mesh_terms":["Disease Susceptibility","Humans","Phenotype","Databases, Factual","Computational Biology","Genetic Predisposition to Disease","Genetic Association Studies","Workflow","Web Browser"],"keywords":["Phenotype","Clinical phenotype","Human genetics","Disease","Bioinformatics","Medicine","Gene","Computational biology","Biology","Genetics","Pathology","drug targets","Common Disease","Natural Language Processing","Rare Disease","Mendelian Diseases","Collaborative Support","Databases, Factual","R","610","QH426-470","Web Browser","Rare diseases","Workflow","Database","Natural language processing (Computer science)","Humans","Genetic Predisposition to Disease","Disease Susceptibility","Genetic disorders","Genetic Association Studies"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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exchange standards (e.g., JSON-LD, FHIR) or persistent identifiers for its own resources."}]},"R":{"name":"Reusable","score":41.67,"criteria":[{"key":"r_license","label":"Clear, open reuse license","kind":"deterministic","weight":1.5,"fraction":0.0,"signal":"no license","rationale":null},{"key":"r_downloads","label":"Demonstrated reuse (downloads)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"downloads=0","rationale":null},{"key":"r_version","label":"Versioned / maintained","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no version chain","rationale":null},{"key":"r_dataset","label":"Classified as a data resource","kind":"deterministic","weight":0.5,"fraction":1.0,"signal":"is_dataset","rationale":null},{"key":"r_reusability","label":"Data-availability statement, license & reproducibility","kind":"llm","weight":2.0,"fraction":0.667,"signal":null,"rationale":"A data availability statement is present with links to code and data, and a CC-BY license is stated for the article, but the license for the underlying data/code is not explicitly detailed, and no reproducibility verification (e.g., test data, workflow) is described."}]}},"suggestions":["Add a metadata schema in JSON-LD or RDF for machine-readability and deposit it in a metadata registry (e.g., BioSharing).","Provide a formal API specification (e.g., OpenAPI) and state if authentication is required for access.","Assign persistent identifiers (e.g., DOIs) to individual datasets or API endpoints beyond the Zenodo record.","Include a separate license for the database content and code (e.g., CC0 for data, MIT for code) in the repository.","Publish a reproducible workflow (e.g., Dockerfile, Jupyter notebook) demonstrating how to regenerate results from raw 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