1
|
Slater LT, Bradlow W, Ball S, Hoehndorf R, Gkoutos GV. Improved characterisation of clinical text through ontology-based vocabulary expansion. J Biomed Semantics 2021; 12:7. [PMID: 33845909 PMCID: PMC8042947 DOI: 10.1186/s13326-021-00241-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 03/18/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Biomedical ontologies contain a wealth of metadata that constitutes a fundamental infrastructural resource for text mining. For several reasons, redundancies exist in the ontology ecosystem, which lead to the same entities being described by several concepts in the same or similar contexts across several ontologies. While these concepts describe the same entities, they contain different sets of complementary metadata. Linking these definitions to make use of their combined metadata could lead to improved performance in ontology-based information retrieval, extraction, and analysis tasks. RESULTS We develop and present an algorithm that expands the set of labels associated with an ontology class using a combination of strict lexical matching and cross-ontology reasoner-enabled equivalency queries. Across all disease terms in the Disease Ontology, the approach found 51,362 additional labels, more than tripling the number defined by the ontology itself. Manual validation by a clinical expert on a random sampling of expanded synonyms over the Human Phenotype Ontology yielded a precision of 0.912. Furthermore, we found that annotating patient visits in MIMIC-III with an extended set of Disease Ontology labels led to semantic similarity score derived from those labels being a significantly better predictor of matching first diagnosis, with a mean average precision of 0.88 for the unexpanded set of annotations, and 0.913 for the expanded set. CONCLUSIONS Inter-ontology synonym expansion can lead to a vast increase in the scale of vocabulary available for text mining applications. While the accuracy of the extended vocabulary is not perfect, it nevertheless led to a significantly improved ontology-based characterisation of patients from text in one setting. Furthermore, where run-on error is not acceptable, the technique can be used to provide candidate synonyms which can be checked by a domain expert.
Collapse
Affiliation(s)
- Luke T. Slater
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT UK
- University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, B15 2TT UK
| | - William Bradlow
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT UK
- University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, B15 2TT UK
| | - Simon Ball
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT UK
- University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, B15 2TT UK
| | - Robert Hoehndorf
- Computational Bioscience Research Centre, KAUST, Thuwal, Saudi Arabia
| | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT UK
- University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, B15 2TT UK
- NIHR Experimental Cancer Medicine Centre, University of Birmingham, Birmingham, B15 2TT UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University of Birmingham, Birmingham, B15 2TT UK
- NIHR Biomedical Research Centre, University of Birmingham, Birmingham, B15 2TT UK
- MRC Health Data Research (HDR), Birmingham, UK
| |
Collapse
|
2
|
Riedel MC, Salo T, Hays J, Turner MD, Sutherland MT, Turner JA, Laird AR. Automated, Efficient, and Accelerated Knowledge Modeling of the Cognitive Neuroimaging Literature Using the ATHENA Toolkit. Front Neurosci 2019; 13:494. [PMID: 31156374 PMCID: PMC6530419 DOI: 10.3389/fnins.2019.00494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 04/29/2019] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging research is growing rapidly, providing expansive resources for synthesizing data. However, navigating these dense resources is complicated by the volume of research articles and variety of experimental designs implemented across studies. The advent of machine learning algorithms and text-mining techniques has advanced automated labeling of published articles in biomedical research to alleviate such obstacles. As of yet, a comprehensive examination of document features and classifier techniques for annotating neuroimaging articles has yet to be undertaken. Here, we evaluated which combination of corpus (abstract-only or full-article text), features (bag-of-words or Cognitive Atlas terms), and classifier (Bernoulli naïve Bayes, k-nearest neighbors, logistic regression, or support vector classifier) resulted in the highest predictive performance in annotating a selection of 2,633 manually annotated neuroimaging articles. We found that, when utilizing full article text, data-driven features derived from the text performed the best, whereas if article abstracts were used for annotation, features derived from the Cognitive Atlas performed better. Additionally, we observed that when features were derived from article text, anatomical terms appeared to be the most frequently utilized for classification purposes and that cognitive concepts can be identified based on similar representations of these anatomical terms. Optimizing parameters for the automated classification of neuroimaging articles may result in a larger proportion of the neuroimaging literature being annotated with labels supporting the meta-analysis of psychological constructs.
Collapse
Affiliation(s)
- Michael C. Riedel
- Department of Physics, Florida International University, Miami, FL, United States
| | - Taylor Salo
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Jason Hays
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Matthew D. Turner
- Psychology and Neuroscience, Georgia State University, Atlanta, GA, United States
| | - Matthew T. Sutherland
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Jessica A. Turner
- Psychology and Neuroscience, Georgia State University, Atlanta, GA, United States
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, United States
| |
Collapse
|
3
|
Yang CJ, Chiang JH. Gene ontology concept recognition using named concept: understanding the various presentations of the gene functions in biomedical literature. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:5146423. [PMID: 30376045 PMCID: PMC6204799 DOI: 10.1093/database/bay115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 09/29/2018] [Indexed: 11/14/2022]
Abstract
OBJECTIVE A major challenge in precision medicine is the development of patient-specific genetic biomarkers or drug targets. The firsthand information of the genes associated with the pathologic pathways of interest is buried in the ocean of biomedical literature. Gene ontology concept recognition (GOCR) is a biomedical natural language processing task used to extract and normalize the mentions of gene ontology (GO), the controlled vocabulary for gene functions across many species, from biomedical text. The previous GOCR systems, using either rule-based or machine-learning methods, treated GO concepts as separate terms and did not have an efficient way of sharing the common synonyms among the concepts. MATERIALS AND METHODS We used the CRAFT corpus in this study. Targeting the compositional structure of the GO, we introduced named concept, the basic conceptual unit which has a conserved name and is used in other complex concepts. Using the named concepts, we separated the GOCR task into dictionary-matching and machine-learning steps. By harvesting the surface names used in the training data, we wildly boosted the synonyms of GO concepts via the connection of the named concepts and then enhanced the capability to recognize more GO concepts in the text. The source code is available athttps://github.com/jeroyang/ncgocr. RESULTS Named concept gene ontology concept recognizer (NCGOCR) achieved 0.804 precision and 0.715 recall by correct recognition of the non-standard mentions of the GO concepts. DISCUSSION The lack of consensus on GO naming causes diversity in the GO mentions in biomedical manuscripts. The high performance is owed to the stability of the composing GO concepts and the lack of variance in the spelling of named concepts. CONCLUSION NCGOCR reduced the arduous work of GO annotation and amended the process of searching for the biomarkers or drug targets, leading to improved biomarker development and greater success in precision medicine.
Collapse
Affiliation(s)
- Chia-Jung Yang
- Department of Computer Science and Information Engineering, National Cheng Kung University, 1, University Road, Tainan City, Taiwan.,Department of Radiology, Taitung Mackay Memorial Hospital, 1, Lane 303, Changsha Street, Taitung City, Taiwan
| | - Jung-Hsien Chiang
- Department of Computer Science and Information Engineering, National Cheng Kung University, 1, University Road, Tainan City, Taiwan
| |
Collapse
|
4
|
Bada M, Vasilevsky N, Baumgartner WA, Haendel M, Hunter LE. Gold-standard ontology-based anatomical annotation in the CRAFT Corpus. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:4780291. [PMID: 31725864 PMCID: PMC7243923 DOI: 10.1093/database/bax087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/25/2017] [Accepted: 10/27/2017] [Indexed: 12/24/2022]
Abstract
Gold-standard annotated corpora have become important resources for the training and testing of natural-language-processing (NLP) systems designed to support biocuration efforts, and ontologies are increasingly used to facilitate curational consistency and semantic integration across disparate resources. Bringing together the respective power of these, the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of full-length, open-access biomedical journal articles with extensive manually created syntactic, formatting and semantic markup, was previously created and released. This initial public release has already been used in multiple projects to drive development of systems focused on a variety of biocuration, search, visualization, and semantic and syntactic NLP tasks. Building on its demonstrated utility, we have expanded the CRAFT Corpus with a large set of manually created semantic annotations relying on Uberon, an ontology representing anatomical entities and life-cycle stages of multicellular organisms across species as well as types of multicellular organisms defined in terms of life-cycle stage and sexual characteristics. This newly created set of annotations, which has been added for v2.1 of the corpus, is by far the largest publicly available collection of gold-standard anatomical markup and is the first large-scale effort at manual markup of biomedical text relying on the entirety of an anatomical terminology, as opposed to annotation with a small number of high-level anatomical categories, as performed in previous corpora. In addition to presenting and discussing this newly available resource, we apply it to provide a performance baseline for the automatic annotation of anatomical concepts in biomedical text using a prominent concept recognition system. The full corpus, released with a CC BY 3.0 license, may be downloaded from http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml. Database URL: http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml
Collapse
Affiliation(s)
- Michael Bada
- School of Medicine, Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., P.O. Box 6511, MS 8303, Aurora, CO 80045-0511, USA
| | - Nicole Vasilevsky
- Ontology Development Group, Library, Oregon Health & Science University, 318 SW Sam Jackson, Park Road, Portland, OR 97239, USA
| | - William A Baumgartner
- School of Medicine, Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., P.O. Box 6511, MS 8303, Aurora, CO 80045-0511, USA
| | - Melissa Haendel
- Ontology Development Group, Library, Oregon Health & Science University, 318 SW Sam Jackson, Park Road, Portland, OR 97239, USA
| | - Lawrence E Hunter
- School of Medicine, Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., P.O. Box 6511, MS 8303, Aurora, CO 80045-0511, USA
| |
Collapse
|
5
|
Basaldella M, Furrer L, Tasso C, Rinaldi F. Entity recognition in the biomedical domain using a hybrid approach. J Biomed Semantics 2017; 8:51. [PMID: 29122011 PMCID: PMC5679148 DOI: 10.1186/s13326-017-0157-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 09/19/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. METHOD The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. RESULTS In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. CONCLUSION These results are to our knowledge the best reported so far in this particular task.
Collapse
Affiliation(s)
- Marco Basaldella
- Università degli Studi di Udine, Via delle Scienze 208, Udine, 33100 Italy
| | - Lenz Furrer
- University of Zurich, Institute of Computational Linguistics and Swiss Institute of Bioinformatics, Andreasstrasse 15, Zürich, CH-8050 Switzerland
| | - Carlo Tasso
- Università degli Studi di Udine, Via delle Scienze 208, Udine, 33100 Italy
| | - Fabio Rinaldi
- University of Zurich, Institute of Computational Linguistics and Swiss Institute of Bioinformatics, Andreasstrasse 15, Zürich, CH-8050 Switzerland
| |
Collapse
|
6
|
Taboada M, Rodriguez H, Gudivada RC, Martinez D. A new synonym-substitution method to enrich the human phenotype ontology. BMC Bioinformatics 2017; 18:446. [PMID: 29017443 PMCID: PMC5635572 DOI: 10.1186/s12859-017-1858-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 10/02/2017] [Indexed: 12/29/2022] Open
Abstract
Background Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks. However, biomedical ontology-based named entity recognition continues to be a major research problem. Results This paper presents an automated synonym-substitution method to enrich the Human Phenotype Ontology (HPO) with new synonyms. The approach is mainly based on both the lexical properties of the terms and the hierarchical structure of the ontology. By scanning the lexical difference between a term and its descendant terms, the method can learn new names and modifiers in order to generate synonyms for the descendant terms. By searching for the exact phrases in MEDLINE, the method can automatically rule out illogical candidate synonyms. In total, 745 new terms were identified. These terms were indirectly evaluated through the concept annotations on a gold standard corpus and also by document retrieval on a collection of abstracts on hereditary diseases. A moderate improvement in the F-measure performance on the gold standard corpus was observed. Additionally, 6% more abstracts on hereditary diseases were retrieved, and this percentage was 33% higher if only the highly informative concepts were considered. Conclusions A synonym-substitution procedure that leverages the HPO hierarchical structure works well for a reliable and automatic extension of the terminology. The results show that the generated synonyms have a positive impact on concept recognition, mainly those synonyms corresponding to highly informative HPO terms. Electronic supplementary material The online version of this article (10.1186/s12859-017-1858-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Maria Taboada
- Department of Electronics & Computer Science, University of Santiago de Compostela, Campus Vida, Santiago de Compostela, 15705, Spain.
| | - Hadriana Rodriguez
- Department of Electronics & Computer Science, University of Santiago de Compostela, Campus Vida, Santiago de Compostela, 15705, Spain
| | | | - Diego Martinez
- Department of Applied Physics, University of Santiago de Compostela, 15705, Santiago de Compostela, Campus Vida, Spain
| |
Collapse
|