Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain

2018 ◽  
Vol 165 ◽  
pp. 117-128 ◽  
Author(s):  
Mazen Alobaidi ◽  
Khalid Mahmood Malik ◽  
Maqbool Hussain
Keyword(s):  
Biomedical Ontologies ◽  
Ontology Generation

Partitioning and local matching learning of large biomedical ontologies

2019 ◽  
Author(s):  
Amir Laadhar ◽  
Faiza Ghozzi ◽  
Imen Megdiche ◽  
Franck Ravat ◽  
Olivier Teste ◽  
...  
Keyword(s):  
Biomedical Ontologies

scholarly journals Quality assurance and enrichment of biological and biomedical ontologies and terminologies

2020 ◽  
Vol 20 (S10) ◽  
Author(s):  
Ankur Agrawal ◽  
Licong Cui
Keyword(s):  
Quality Assurance ◽  
Cancer Registries ◽  
Supplement Issue ◽  
Biomedical Ontologies ◽  
Snomed Ct ◽  
Unified Medical Language System ◽  
Domain Specific ◽  
Healthcare Settings ◽  
Domain Specific Knowledge ◽  
Enrichment Techniques

AbstractBiological and biomedical ontologies and terminologies are used to organize and store various domain-specific knowledge to provide standardization of terminology usage and to improve interoperability. The growing number of such ontologies and terminologies and their increasing adoption in clinical, research and healthcare settings call for effective and efficient quality assurance and semantic enrichment techniques of these ontologies and terminologies. In this editorial, we provide an introductory summary of nine articles included in this supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. The articles cover a range of standards including SNOMED CT, National Cancer Institute Thesaurus, Unified Medical Language System, North American Association of Central Cancer Registries and OBO Foundry Ontologies.

scholarly journals Tackling the challenges of matching biomedical ontologies

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Daniel Faria ◽  
Catia Pesquita ◽  
Isabela Mott ◽  
Catarina Martins ◽  
Francisco M. Couto ◽  
...  
Keyword(s):  
Biomedical Ontologies

scholarly journals BO-LSTM: Classifying relations via long short-term memory networks along biomedical ontologies

2018 ◽  
Author(s):  
Andre Lamurias ◽  
Luka A. Clarke ◽  
Francisco M. Couto
Keyword(s):  
Deep Learning ◽  
Text Mining ◽  
Drug Interactions ◽  
Short Term Memory ◽  
Biomedical Ontologies ◽  
Short Term ◽  
Term Memory ◽  
Domain Specific ◽  
Learning Techniques ◽  
Long Short Term Memory

AbstractRecent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.We propose a new model, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using an open biomedical ontology, which in our case-study was Chemical Entities of Biological Interest (ChEBI). We assessed the performance of BO-LSTM on detecting and classifying drug-drug interactions in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved both the F1-score of the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. Our findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.Author summaryA high quantity of biomedical information is only available in documents such as scientific articles and patents. Due to the rate at which new documents are produced, we need automatic methods to extract useful information from them. Text mining is a subfield of information retrieval which aims at extracting relevant information from text. Scientific literature is a challenge to text mining because of the complexity and specificity of the topics approached. In recent years, deep learning has obtained promising results in various text mining tasks by exploring large datasets. On the other hand, ontologies provide a detailed and sound representation of a domain and have been developed to diverse biomedical domains. We propose a model that combines deep learning algorithms with biomedical ontologies to identify relations between concepts in text. We demonstrate the potential of this model to extract drug-drug interactions from abstracts and drug descriptions. This model can be applied to other biomedical domains using an annotated corpus of documents and an ontology related to that domain to train a new classifier.

scholarly journals Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease

2020 ◽  
Author(s):  
Mary E. Dolan ◽  
David P. Hill ◽  
Gaurab Mukherjee ◽  
Monica S. McAndrews ◽  
Elissa J. Chesler ◽  
...  
Keyword(s):  
Lung Diseases ◽  
Bioinformatics Analysis ◽  
Current Knowledge ◽  
Viral Disease ◽  
Mechanisms Of Action ◽  
Biomedical Ontologies ◽  
Phenotypic Analysis ◽  
Gene Sets ◽  
Significant Enrichment ◽  
Intense Research

AbstractThe emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.

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