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Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems [article]

Wasila Dahdul, Prashanti Manda, Hong Cui, James P Balhoff, Alexander Dececchi, Nizar Ibrahim, Hilmar Lapp, Todd Vision, Paula M Mabee
2018 bioRxiv   pre-print
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

Wasila Dahdul, Prashanti Manda, Hong Cui, James P Balhoff, T Alexander Dececchi, Nizar Ibrahim, Hilmar Lapp, Todd Vision, Paula M Mabee
2018 Database: The Journal of Biological Databases and Curation  
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]

Jingqing Zhang, Luis Bolanos, Tong Li, Ashwani Tanwar, Guilherme Freire, Xian Yang, Julia Ive, Vibhor Gupta, Yike Guo
2021 arXiv   pre-print
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]

Jingqing Zhang, Luis Bolanos, Ashwani Tanwar, Julia Ive, Vibhor Gupta, Yike Guo
2021 arXiv   pre-print
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]

Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang, Minhong Wang, Emma Whitfield, Honghan Wu
2021 arXiv   pre-print
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

YVES LUSSIER, TARA BORLAWSKY, DANIEL RAPPAPORT, YANG LIU, CAROL FRIEDMAN
2005 Biocomputing 2006  
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

Yves Lussier, Tara Borlawsky, Daniel Rappaport, Yang Liu, Carol Friedman
2006 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
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

Michaela Gündel, Erfan Younesi, Ashutosh Malhotra, Jiali Wang, Hui Li, Bijun Zhang, Bernard de Bono, Heinz-Theodor Mevissen, Martin Hofmann-Apitius
2013 Journal of Biomedical Semantics  
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]

Ling Luo, Shankai Yan, Po-Ting Lai, Daniel Veltri, Andrew Oler, Sandhya Xirasagar, Rajarshi Ghosh, Morgan Similuk, Peter N. Robinson, Zhiyong Lu
2020 arXiv   pre-print
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]

Sara Althubaiti, Andreas Karwath, Ashraf Dallol, Adeeb Noor, Shadi Salem Alkhayyat, Rolina Alwassia, Katsuhiko Mineta, Takashi Gojobori, Andrew D Beggs, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf
2019 bioRxiv   pre-print
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

Andre Lamurias, Diana Sousa, Luka A. Clarke, Francisco M. Couto
2019 BMC Bioinformatics  
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

T. Groza, S. Kohler, S. Doelken, N. Collier, A. Oellrich, D. Smedley, F. M. Couto, G. Baynam, A. Zankl, P. N. Robinson
2015 Database: The Journal of Biological Databases and Curation  
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]

Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang, Minhong Wang, Arlene Casey, Emma Davidson, Jiaoyan Chen, Beatrice Alex, William Whiteley, Honghan Wu
2022 arXiv   pre-print
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]

Francesco Moramarco, Damir Juric, Aleksandar Savkov, Jack Flann, Maria Lehl, Kristian Boda, Tessa Grafen, Vitalii Zhelezniak, Sunir Gohil, Alex Papadopoulos Korfiatis, Nils Hammerla
2021 arXiv   pre-print
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

Ling Luo, Shankai Yan, Po-Ting Lai, Daniel Veltri, Andrew Oler, Sandhya Xirasagar, Rajarshi Ghosh, Morgan Similuk, Peter N Robinson, Zhiyong Lu
2021 Bioinformatics  
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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