Ontology based mining of pathogen–disease associations from literature

Kafkas, Şenay; Hoehndorf, Robert

doi:10.1186/s13326-019-0208-2

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Among this work we can point to the Bacteria Biotope challenge task at the BioNLP shared tasks [ 15 ], which focuses on certain bacteria and their habitats and phenotypes, including 491 individual microorganisms mentioned in 392 articles. There is previous work using the literature to identify the relation of pathogens to the environment [ 16 ], pathogen-disease prediction using ontologies and literature mining [ 17 ], identification of the geolocation of pathogen samples (e.g. GeoBoost [ 18 , 19 ]) for phylogeography or other aspects of pathogens related to biodiversity [ 20 , 21 ], in addition to toxins [ 13 , 22 ].…”

Section: Related Workmentioning

confidence: 99%

Classifying literature mentions of biological pathogens as experimentally studied using natural language processing

Yepes

Verspoor

2023

J Biomed Semant

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Background Information pertaining to mechanisms, management and treatment of disease-causing pathogens including viruses and bacteria is readily available from research publications indexed in MEDLINE. However, identifying the literature that specifically characterises these pathogens and their properties based on experimental research, important for understanding of the molecular basis of diseases caused by these agents, requires sifting through a large number of articles to exclude incidental mentions of the pathogens, or references to pathogens in other non-experimental contexts such as public health. Objective In this work, we lay the foundations for the development of automatic methods for characterising mentions of pathogens in scientific literature, focusing on the task of identifying research that involves the experimental study of a pathogen in an experimental context. There are no manually annotated pathogen corpora available for this purpose, while such resources are necessary to support the development of machine learning-based models. We therefore aim to fill this gap, producing a large data set automatically from MEDLINE under some simplifying assumptions for the task definition, and using it to explore automatic methods that specifically support the detection of experimentally studied pathogen mentions in research publications. Methods We developed a pathogen mention characterisation literature data set —READBiomed-Pathogens— automatically using NCBI resources, which we make available. Resources such as the NCBI Taxonomy, MeSH and GenBank can be used effectively to identify relevant literature about experimentally researched pathogens, more specifically using MeSH to link to MEDLINE citations including titles and abstracts with experimentally researched pathogens. We experiment with several machine learning-based natural language processing (NLP) algorithms leveraging this data set as training data, to model the task of detecting papers that specifically describe experimental study of a pathogen. Results We show that our data set READBiomed-Pathogens can be used to explore natural language processing configurations for experimental pathogen mention characterisation. READBiomed-Pathogens includes citations related to organisms including bacteria, viruses, and a small number of toxins and other disease-causing agents. Conclusions We studied the characterisation of experimentally studied pathogens in scientific literature, developing several natural language processing methods supported by an automatically developed data set. As a core contribution of the work, we presented a methodology to automatically construct a data set for pathogen identification using existing biomedical resources. The data set and the annotation code are made publicly available. Performance of the pathogen mention identification and characterisation algorithms were additionally evaluated on a small manually annotated data set shows that the data set that we have generated allows characterising pathogens of interest. Trial registration N/A.

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Section: Related Workmentioning

confidence: 99%

Classifying literature mentions of biological pathogens as experimentally studied using natural language processing

Yepes

Verspoor

2023

J Biomed Semant

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“…We further include information on known mechanisms of drug resistance for 30 pathogens representing 78 pathogen-disease associations. While PathoPhenoDB is largely based on manually curated information, we also extracted pathogen-disease associations from the biomedical literature [20] and use this information to enrich the content of our database. Statistics relevant to the textmined content is available from the web site http://patho.phenomebrowser.net.…”

Section: Data Recordsmentioning

confidence: 99%

“…PathoPhenoDB is a database which relies on pathogen-disease associations curated manually from public resources and the scientific literature. We further expanded the pathogen-disease associations by complementary textmined data [20]. PathoPhen-oDB links pathogens to their phenotypes based on manually-curated and textmined disease-phenotype associations.…”

mentioning

confidence: 99%

PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

Kafkas

Abdelhakim

Hashish

et al. 2018

Preprint

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Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate phenotypes with infectious disease. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http: //patho.phenomebrowser.net/, and the data is freely available through a public SPARQL endpoint.

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“…Ontology ( Bandrowski et al, 2016 ) and knowledge graph ( Nicholson and Greene, 2020 ) can provide structured, computable organization and management of large amounts of data. Several biomedical ontologies have been proven useful in biomedical text mining studies ( Shen and Lee, 2016 ; Kafkas and Hoehndorf, 2019 ), including Disease Ontology (2023) , Human Phenotype Ontology (HPO) (2023), UMLS, etc. Different ontologies can also be constructed based on their research objectives.…”

Section: Introductionmentioning

confidence: 99%

Drug–disease association prediction with literature based multi-feature fusion

Kang

Hou

et al. 2023

Front. Pharmacol.

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Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge.Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism.Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916.Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

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Ontology based mining of pathogen–disease associations from literature

Cited by 8 publications

References 18 publications

Classifying literature mentions of biological pathogens as experimentally studied using natural language processing

Classifying literature mentions of biological pathogens as experimentally studied using natural language processing

PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

Drug–disease association prediction with literature based multi-feature fusion

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