PhenCards: a data resource linking human phenotype information to biomedical knowledge

Havrilla, James M; Liu, Cong; Dong, Xiangchen; Weng, Chunhua; Wang, Kai

doi:10.1186/s13073-021-00909-8

Cited by 8 publications

(4 citation statements)

References 43 publications

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“… 1 , 4 , 5 For example, a number of computational methods have been developed to facilitate phenotype-based prioritization of disease variants and genes, 6 , 7 , 8 , 9 , 10 , 11 , 12 and some methods also enable the prediction of Mendelian diseases directly from phenotype information. 13 , 14 , 15 To facilitate computational phenotype analysis, the Human Phenotype Ontology (HPO) was established, which provides a standardized vocabulary to describe phenotypic abnormalities in human diseases. 16 The current release of HPO (June 2022) covers 13,000 terms and over 156,000 annotations on hereditary diseases.…”

Section: Introductionmentioning

confidence: 99%

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

Yang,

Liu,

Deng

et al. 2024

Patterns

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Section: Introductionmentioning

confidence: 99%

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

Yang,

Liu,

Deng

et al. 2024

Patterns

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“…Thereby DrugCentral has become an essential resource for the scientific community, firmly linked to well-established resources, e.g. UniProt ( 4 ), ChEBI ( 5 ), Guide to Pharmacology ( 6 ), UniChem ( 7 ), Probes & Drugs portal (P&D) ( 8 ), PhenCards ( 9 ), COVID19db ( 10 ), etc. In addition, DrugCentral has become an essential component of the Knowledge Management Center KMC Datasets and Tools ( 11 ) in the NIH Common Fund's Illuminating the Druggable Genome (IDG) consortium ( https://commonfund.nih.gov/idg ).…”

Section: Introductionmentioning

confidence: 99%

DrugCentral 2023 extends human clinical data and integrates veterinary drugs

Avram

Wilson

Curpăn

et al. 2022

Nucleic Acids Research

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DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs (154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance data for pediatric and geriatric patients. Additional enhancements include chemical substructure searching using SMILES and ‘Target Cards’ based on UniProt accession codes. Statistics of interests include the following: (i) 60% of the covered drugs are on-market drugs with expired patent and exclusivity coverage, 17% are off-market, and 23% are on-market drugs with active patents and exclusivity coverage; (ii) 59% of the drugs are oral, 33% are parenteral and 18% topical, at the level of the active ingredients; (iii) only 3% of all drugs are for animal use only; however, 61% of the veterinary drugs are also approved for human use; (iv) dogs, cats and horses are by far the most represented target species for veterinary drugs; (v) the physicochemical property profile of animal drugs is very similar to that of human drugs. Use cases include azaperone, the only sedative approved for swine, and ruxolitinib, a Janus kinase inhibitor.

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“…Tracking the change of phenotype expression over time in a patient can provide key insights that allow for earlier diagnosis, improved prognosis, and individualized treatment decision making [1–2] . In addition to structured information, one major source of information on clinical phenotypes for patients are unstructured texts from electronic health records (EHRs).…”

Section: Introductionmentioning

confidence: 99%

Termviewer – A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation

Nixon

Havrilla

et al. 2022

Chemistry & Biodiversity

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Clinical notes from electronic health records (EHRs) contain a large amount of clinical phenotype data on patients that can provide insights into the phenotypic presentation of various diseases. A number of Natural Language Processing (NLP) algorithms have been utilized in the past few years to annotate medical concepts, such as Human Phenotype Ontology (HPO) terms, from clinical notes. However, efficient use of NLP algorithms requires the use of high‐quality clinical notes with phenotype descriptions, and erroneous annotations often exist in results from these NLP algorithms. Manual review by human experts is often needed to compile the correct phenotype information on individual patients. Here we develop TermViewer, a web application that allows multi‐party collaborative annotation and quality assessment of clinical notes that have already been processed and tagged by NLP algorithms. TermViewer allows users to view clinical notes with HPO terms highlighted, and to easily classify high‐quality notes and revise incorrect tagging of HPO terms. Currently, TermViewer combines MetaMap and cTAKES, two of the most widely used NLP tools for tagging medical terms, and identifies where these two tools agree and disagree, allowing users to perform collaborative manual reviews of computationally generated HPO annotations. TermViewer can be a stand‐alone tool for analyzing notes or become part of a machine‐learning pipeline where tagged HPO terms can be used as additional input data. TermViewer is available at https://github.com/WGLab/TermViewer.

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PhenCards: a data resource linking human phenotype information to biomedical knowledge

Cited by 8 publications

References 43 publications

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

DrugCentral 2023 extends human clinical data and integrates veterinary drugs

Termviewer – A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation

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