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Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems
[article]
2018
bioRxiv
pre-print
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Most existing Natural Language Processing tools stop at entity recognition, leaving a need for tools that can assist with both aspects of the task. ...
We describe the first expert-curated Gold Standard corpus for ontology-based annotation of phenotypes from the systematics literature. ...
Fernando and L. Jackson provided valuable feedback on the Gold Standard and Phenoscape Guide to Character Annotation. We thank D. Blackburn for his helpful comments on this manuscript. ...
doi:10.1101/322156
fatcat:xsnyvtqwlrgm5c7m24zyv2axee
Annotation of phenotypes using ontologies: a gold standard for the training and evaluation of natural language processing systems
2018
Database: The Journal of Biological Databases and Curation
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Natural language processing tools have been developed to facilitate this task, and the training and evaluation of these tools depend on the availability of high quality, manually annotated gold standard ...
These findings point toward ways to better design software to augment human curators and the use of the gold standard corpus will allow training and assessment of new tools to improve phenotype annotation ...
Fernando and L. Jackson provided valuable feedback on the gold standard and Phenoscape Guide to Character Annotation. We thank D. Blackburn for his helpful comments on this manuscript. ...
doi:10.1093/database/bay110
pmid:30576485
pmcid:PMC6301375
fatcat:pooqfubonndjndf3c5sarxpjzu
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
[article]
2021
arXiv
pre-print
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The extrinsic evaluation on three ICU benchmarks also shows the benefit of using the phenotypes annotated by our model as features. ...
Our approach achieves a new SOTA for the unsupervised phenotype concept annotation on clinical text on F1 and Recall outperforming the previous SOTA with a gain of up to 4.5 and 4.0 absolute points, respectively ...
Deepa (M.R.S.H) and Dr. Ashok (M.S.) for helping us create gold-standard phenotype annotation data. We would also like to thank the four anonymous reviewers for the feedback. ...
arXiv:2109.01935v1
fatcat:yl5ah3oo7bgvvn6cubivjukkli
Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases
[article]
2021
arXiv
pre-print
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Methods: We develop a novel phenotype annotation model to annotate phenotypic features of patients which are then used as input features of predictive models to predict ICU patient outcomes. ...
We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to ...
Ashok (M.S.) for helping us create gold-standard phenotype annotation data. ...
arXiv:2107.11665v2
fatcat:2n2brd7xbbhmxg5sozgqmx23ve
Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision
[article]
2021
arXiv
pre-print
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The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical ...
We propose a method using ontologies and weak supervision. ...
ACKNOWLEDGEMENT The authors would like to thank the comments from Dr William Whiteley and other members in the Clinical Natural Language Processing Research Group, University of Edinburgh. ...
arXiv:2105.01995v3
fatcat:yjs2rd7lszbmxauoiqopjsxlqi
PHENOGO: ASSIGNING PHENOTYPIC CONTEXT TO GENE ONTOLOGY ANNOTATIONS WITH NATURAL LANGUAGE PROCESSING
2005
Biocomputing 2006
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Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. ...
In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and ...
The authors thank Lyudmila Shagina and Jianrong Li for their respective contribution in the development of BioMedLEE and PhenOS. ...
doi:10.1142/9789812701626_0007
fatcat:b34ify2dizcsdnxhiui22o55e4
PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing
2006
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. ...
In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and ...
The authors thank Lyudmila Shagina and Jianrong Li for their respective contribution in the development of BioMedLEE and PhenOS. ...
pmid:17094228
pmcid:PMC2906243
fatcat:gohxa3cx6nbt5fmcjrnk5hkkpe
HuPSON: the human physiology simulation ontology
2013
Journal of Biomedical Semantics
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The ontology is based on the Basic Formal Ontology, and adheres to the MIREOT principles; the constructed ontology has been evaluated via structural features, competency questions and use case scenarios ...
Results: We propose a first version of a newly constructed ontology, HuPSON, as a basis for shared semantics and interoperability of simulations, of models, of algorithms and of other resources in this ...
Acknowledgements This work was conducted using the Protégé resource, which is supported by grant LM007885 from the United States National Library of Medicine. ...
doi:10.1186/2041-1480-4-35
pmid:24267822
pmcid:PMC4177144
fatcat:zjjzvomhdvhlnpt3n4hrczcgvu
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology
[article]
2020
arXiv
pre-print
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Then, the dictionary and biomedical literature are used to automatically build a weakly-supervised training dataset for machine learning. ...
In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ...
Acknowledgements This research is supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine. Conflict of Interest: none declared. ...
arXiv:2009.08478v1
fatcat:66acp6ny3jbllevwmdz5thkl6y
Ontology-based prediction of cancer driver genes
[article]
2019
bioRxiv
pre-print
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We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing. ...
We have developed a novel method for identifying cancer driver genes. ...
processes, and phenotypes in the form of logical axioms and natural language 78 definitions [14] . ...
doi:10.1101/561480
fatcat:fbh7qv6x5bfaxb2nwcnh6vpnoa
BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies
2019
BMC Bioinformatics
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By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved the F1-score of both the detection and classification of drug-drug interactions, particularly in a document ...
Furthermore, we developed and made available a corpus of 228 abstracts annotated with relations between genes and phenotypes, and demonstrated how BO-LSTM can be applied to other types of relations. ...
Availability of data and materials The data and code used for this study are available at https://github.com/ lasigeBioTM/BOLSTM. ...
doi:10.1186/s12859-018-2584-5
fatcat:2c2faf4jengjdf7eptr2nqfxgu
Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora
2015
Database: The Journal of Biological Databases and Curation
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standard for free text annotation of human phenotypes. ...
However, phenotypes only recently became an entity of interest for specialized concept recognition systems, and hardly any annotated text is available for performance testing and training. ...
Conflict of interest. None declared. ...
doi:10.1093/database/bav005
pmid:25725061
pmcid:PMC4343077
fatcat:6bc3qcxskfbybaevdtjc3u5nra
Ontology-Based and Weakly Supervised Rare Disease Phenotyping from Clinical Notes
[article]
2022
arXiv
pre-print
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We evaluated the approach on three clinical datasets of discharge summaries and radiology reports from two institutions in the US and the UK. ...
The ontology-based framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition ...
ACKNOWLEDGEMENT We would like to thank Emma Whitfield for the important support on data annotations of discharge summaries during the previous study [7] and feedback on the writing of this work. ...
arXiv:2205.05656v1
fatcat:3phw6sdyafapfd4scax7kbg77m
Towards more patient friendly clinical notes through language models and ontologies
[article]
2021
arXiv
pre-print
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Also, we define a novel text simplification metric and evaluation framework, which we use to conduct a large-scale human evaluation of our method against the state of the art. ...
Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art. ...
We also include the Consumer Health Vocabulary (CHV), the purpose of which is lexical simplification 25 , and the Human Phenotype Ontology (HPO), which is a standardized vocabulary of phenotypic abnormalities ...
arXiv:2112.12672v1
fatcat:njwnkr25vbcx5fvm4zuupla27i
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology
2021
Bioinformatics
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In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ...
However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation. ...
Acknowledgements This research is supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine. ...
doi:10.1093/bioinformatics/btab019
pmid:33471061
fatcat:sxba74g5azgfno2lz2yrwnm5eu
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