Journal of Biomedical Semantics最新文献

{"title":"Notes on Iterated Rationality Models of Scalar Implicatures","authors":"D. Fox, Roni Katzir","doi":"10.1093/jos/ffab015","DOIUrl":"https://doi.org/10.1093/jos/ffab015","url":null,"abstract":"","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"26 1","pages":"571-600"},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88721250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All Focus is Contrastive: On Polarity (Verum) Focus, Answer Focus, Contrastive Focus and Givenness","authors":"Daniel Goodhue","doi":"10.1093/jos/ffab018","DOIUrl":"https://doi.org/10.1093/jos/ffab018","url":null,"abstract":"","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"3 1","pages":"117-158"},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75876273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Role of Focus-Sensitivity for a Typology of Presupposition Triggers","authors":"Alexander Göbel","doi":"10.1093/jos/ffac011","DOIUrl":"https://doi.org/10.1093/jos/ffac011","url":null,"abstract":"","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"26 1","pages":"617-656"},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82412468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prefrontal fNIRS-based clinical data analysis of brain functions in individuals abusing different types of drugs.","authors":"Xuelin Gu,&nbsp;Banghua Yang,&nbsp;Shouwei Gao,&nbsp;Lin Feng Yan,&nbsp;Ding Xu,&nbsp;Wen Wang","doi":"10.1186/s13326-021-00256-y","DOIUrl":"https://doi.org/10.1186/s13326-021-00256-y","url":null,"abstract":"<p><strong>Background: </strong>The activation degree of the orbitofrontal cortex (OFC) functional area in drug abusers is directly related to the craving for drugs and the tolerance to punishment. Currently, among the clinical research on drug rehabilitation, there has been little analysis of the OFC activation in individuals abusing different types of drugs, including heroin, methamphetamine, and mixed drugs. Therefore, it becomes urgently necessary to clinically investigate the abuse of different drugs, so as to explore the effects of different types of drugs on the human brain.</p><p><strong>Methods: </strong>Based on prefrontal high-density functional near-infrared spectroscopy (fNIRS), this research designs an experiment that includes resting and drug addiction induction. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed drugs) were collected using fNIRS and analyzed by combining algorithm and statistics.</p><p><strong>Results: </strong>Linear discriminant analysis (LDA), Support vector machine (SVM) and Machine-learning algorithm was implemented to classify different drug abusers. Oxygenated hemoglobin (HbO2) activations in the OFC of different drug abusers were statistically analyzed, and the differences were confirmed. Innovative findings: in both the Right-OFC and Left-OFC areas, methamphetamine abusers had the highest degree of OFC activation, followed by those abusing mixed drugs, and heroin abusers had the lowest. The same result was obtained when OFC activation was investigated without distinguishing the left and right hemispheres.</p><p><strong>Conclusions: </strong>The findings confirmed the significant differences among different drug abusers and the patterns of OFC activations, providing a theoretical basis for personalized clinical treatment of drug rehabilitation in the future.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"21"},"PeriodicalIF":1.9,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39926571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ontology network for Diabetes Mellitus in Mexico.","authors":"Cecilia Reyes-Peña,&nbsp;Mireya Tovar,&nbsp;Maricela Bravo,&nbsp;Regina Motz","doi":"10.1186/s13326-021-00252-2","DOIUrl":"https://doi.org/10.1186/s13326-021-00252-2","url":null,"abstract":"<p><strong>Background: </strong>Medical experts in the domain of Diabetes Mellitus (DM) acquire specific knowledge from diabetic patients through monitoring and interaction. This allows them to know the disease and information about other conditions or comorbidities, treatments, and typical consequences of the Mexican population. This indicates that an expert in a domain knows technical information about the domain and contextual factors that interact with it in the real world, contributing to new knowledge generation. For capturing and managing information about the DM, it is necessary to design and implement techniques and methods that allow: determining the most relevant conceptual dimensions and their correct organization, the integration of existing medical and clinical information from different resources, and the generation of structures that represent the deduction process of the doctor. An Ontology Network is a collection of ontologies of diverse knowledge domains which can be interconnected by meta-relations. This article describes an Ontology Network for representing DM in Mexico, designed by a proposed methodology. The information used for Ontology Network building include the ontological resource reuse and non-ontological resource transformation for ontology design and ontology extending by natural language processing techniques. These are medical information extracted from vocabularies, taxonomies, medical dictionaries, ontologies, among others. Additionally, a set of semantic rules has been defined within the Ontology Network to derive new knowledge.</p><p><strong>Results: </strong>An Ontology Network for DM in Mexico has been built from six well-defined domains, resulting in new classes, using ontological and non-ontological resources to offer a semantic structure for assisting in the medical diagnosis process. The network comprises 1367 classes, 20 object properties, 63 data properties, and 4268 individuals from seven different ontologies. Ontology Network evaluation was carried out by verifying the purpose for its design and some quality criteria.</p><p><strong>Conclusions: </strong>The composition of the Ontology Network offers a set of well-defined ontological modules facilitating the reuse of one or more of them. The inclusion of international vocabularies as SNOMED CT or ICD-10 reinforces the representation by international standards. It increases the semantic interoperability of the network, providing the opportunity to integrate other ontologies with the same vocabularies. The ontology network design methodology offers a guide for ontology developers about how to use ontological and non-ontological resources in order to exploit the maximum of information and knowledge from a set of domains that share or not information.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"19"},"PeriodicalIF":1.9,"publicationDate":"2021-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39498460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CIDO ontology updates and secondary analysis of host responses to COVID-19 infection based on ImmPort reports and literature.","authors":"Anthony Huffman, Anna Maria Masci, Jie Zheng, Nasim Sanati, Timothy Brunson, Guanming Wu, Yongqun He","doi":"10.1186/s13326-021-00250-4","DOIUrl":"10.1186/s13326-021-00250-4","url":null,"abstract":"<p><strong>Background: </strong>With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge. As many studies are published within a short span of time, we often lose an integrative and comprehensive picture of host-coronavirus interaction (HCI) mechanisms. As of early April 2021, the ImmPort database has stored 7 studies (with 6 having details) that cover topics including molecular immune signatures, epitopes, and sex differences in terms of mortality in COVID-19 patients. The Coronavirus Infectious Disease Ontology (CIDO) represents basic HCI information. We hypothesize that the CIDO can be used as the platform to represent newly recorded information from ImmPort leading the reinforcement of CIDO.</p><p><strong>Methods: </strong>The CIDO was used as the semantic platform for logically modeling and representing newly identified knowledge reported in the 6 ImmPort studies. A recursive eXtensible Ontology Development (XOD) strategy was established to support the CIDO representation and enhancement. Secondary data analysis was also performed to analyze different aspects of the HCI from these ImmPort studies and other related literature reports.</p><p><strong>Results: </strong>The topics covered by the 6 ImmPort papers were identified to overlap with existing CIDO representation. SARS-CoV-2 viral S protein related HCI knowledge was emphasized for CIDO modeling, including its binding with ACE2, mutations causing different variants, and epitope homology by comparison with other coronavirus S proteins. Different types of cytokine signatures were also identified and added to CIDO. Our secondary analysis of two cohort COVID-19 studies with cytokine panel detection found that a total of 11 cytokines were up-regulated in female patients after infection and 8 cytokines in male patients. These sex-specific gene responses were newly modeled and represented in CIDO. A new DL query was generated to demonstrate the benefits of such integrative ontology representation. Furthermore, IL-10 signaling pathway was found to be statistically significant for both male patients and female patients.</p><p><strong>Conclusion: </strong>Using the recursive XOD strategy, six new ImmPort COVID-19 studies were systematically reviewed, the results were modeled and represented in CIDO, leading to the enhancement of CIDO. The enhanced ontology and further seconary analysis supported more comprehensive understanding of the molecular mechanism of host responses to COVID-19 infection.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"12 1","pages":"18"},"PeriodicalIF":1.6,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10217341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking common human diseases to their phenotypes; development of a resource for human phenomics.","authors":"Şenay Kafkas, Sara Althubaiti, Georgios V Gkoutos, Robert Hoehndorf, Paul N Schofield","doi":"10.1186/s13326-021-00249-x","DOIUrl":"10.1186/s13326-021-00249-x","url":null,"abstract":"<p><strong>Background: </strong>In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings.</p><p><strong>Methods: </strong>We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10-phenotype associations from existing resources containing known relationships.</p><p><strong>Results: </strong>We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease-phenotype associations, measuring their similarity to disease-phenotype associations found in public databases, and assessing how well they could be used to recover gene-disease associations using phenotype similarity.</p><p><strong>Conclusion: </strong>We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at https://doi.org/10.5281/zenodo.4726713 .</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"17"},"PeriodicalIF":1.6,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8383460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39338400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Syntax-based transfer learning for the task of biomedical relation extraction.","authors":"Joël Legrand,&nbsp;Yannick Toussaint,&nbsp;Chedy Raïssi,&nbsp;Adrien Coulet","doi":"10.1186/s13326-021-00248-y","DOIUrl":"https://doi.org/10.1186/s13326-021-00248-y","url":null,"abstract":"<p><strong>Background: </strong>Transfer learning aims at enhancing machine learning performance on a problem by reusing labeled data originally designed for a related, but distinct problem. In particular, domain adaptation consists for a specific task, in reusing training data developedfor the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because they usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains.</p><p><strong>Results: </strong>In this paper, we experiment with transfer learning for the task of relation extraction from biomedical texts, using the TreeLSTM model. We empirically show the impact of TreeLSTM alone and with domain adaptation by obtaining better performances than the state of the art on two biomedical relation extraction tasks and equal performances for two others, for which little annotated data are available. Furthermore, we propose an analysis of the role that syntactic features may play in transfer learning for relation extraction.</p><p><strong>Conclusion: </strong>Given the difficulty to manually annotate corpora in the biomedical domain, the proposed transfer learning method offers a promising alternative to achieve good relation extraction performances for domains associated with scarce resources. Also, our analysis illustrates the importance that syntax plays in transfer learning, underlying the importance in this domain to privilege approaches that embed syntactic features.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"16"},"PeriodicalIF":1.9,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39325342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a systematic conflict resolution framework for ontologies.","authors":"C Maria Keet,&nbsp;Rolf Grütter","doi":"10.1186/s13326-021-00246-0","DOIUrl":"https://doi.org/10.1186/s13326-021-00246-0","url":null,"abstract":"<p><strong>Background: </strong>The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to limitations in the logic or the subject domain semantics. Examples are dealing with different foundational ontologies in ontology alignment and OWL 2 DL's transitive object property versus a qualified cardinality constraint. Such conflicts have to be resolved somehow. However, only isolated and fragmented guidance for doing so is available, which therefore results in ad hoc decision-making that may not be the best choice or forgotten about later.</p><p><strong>Results: </strong>This work aims to address this by taking steps towards a framework to deal with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. It proposes an initial library of common conflicts, a conflict set, typical steps toward resolution, and the software availability and requirements needed for it. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak, being avian influenza, and running examples with COVID-19 ontologies.</p><p><strong>Conclusions: </strong>The evaluation demonstrated the potential and feasibility of a conflict resolution framework for ontologies.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"15"},"PeriodicalIF":1.9,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39295823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Origin of Cross-Cultural Differences in Referential Intuitions: Perspective Taking in the Gödel Case","authors":"Jincai Li","doi":"10.1093/jos/ffab010","DOIUrl":"https://doi.org/10.1093/jos/ffab010","url":null,"abstract":"How do proper names refer? This question about reference is critical for philosophers studying language, linguists investigating meaning and reference, and psycholinguists interested in how children acquire names. Over the past century, philosophers have put forward two classical theories to explain the link between a name and the entity it refers to, i.e., the descriptivist theory proposed by Frege (1892/1948), Russell (1905) and Searle (1958) among others, and the causal-historical view most notably advocated by Kripke (1980). On the former account, a name gets its referent through associated definite descriptions. Thus, when a speaker uses a name, they typically refer to whoever best fits the descriptive content attached to that name. For instance, the name “Kamala Harris” refers to the lady Kamala Harris because she is the sole individual who could uniquely satisfy the descriptive content “the first female vice president of the United States” that is commonly associated with the name nowadays. In contrast, according to the Kripkean causal-historical view, a name refers to a person via a link that is originated in the initial naming ceremony and then gets passed down through a community of speakers. Kripke contends that proper names are rigid designators and they continue to refer to the individuals who were initially given the name, even when they turn out to have none of the properties that speakers associate with this name (1980). That means, on the causal-historical picture, the name “Kamala Harris” would still refer to the person Kamala Harris even if she had not been elected the vice president of the United States. In the philosophical literature, the received wisdom is that Kripke supported his causalhistorical view of reference with the famous “Gödel” thought experiment. Suppose the only thing most people have heard about the mathematician Kurt Gödel is that he is the person who proved the incompleteness of arithmetic, which thus is the only possible definite description these people could associate with Gödel. And now imagine that the person who bears this name (Kurt Gödel) didn’t actually prove the theorem, but instead stole it from a fellow named Schmidt who did all the work. In this case, the descriptivist theory predicts that the name “Gödel” would refer to Schmidt, because Schmidt is the person best fitting","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"64 1","pages":"415-440"},"PeriodicalIF":1.9,"publicationDate":"2021-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74534964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}