CEUR workshop proceedings最新文献

{"title":"Ontology-based representation and analysis of conditional vaccine immune responses using Omics data.","authors":"Anthony Huffman, Edison Ong, Tim Brunson, Nasim Sanati, Jie Zheng, Anna Maria Masci, Guanming Wu, Yongqun He","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>ImmPort, the world's largest repository of immunology data, includes many vaccine immune response datasets. ImmPort maps the metadata of these studies to ontology and database schema. As of February 28, 2023, our ImmPort data analysis identified 6.258 immune exposures using 47 vaccines in 4,607 human subjects, and 324 cohort studies from the ImmPort. We hypothesized that an integrative ontological representation of the data from these studies would enhance our understanding and analysis of these ImmPort vaccine studies, and with ontological classification and tools such as VIGET, we could further study the effects of different conditions such as vaccine types and host biological sex on the vaccine response gene expression profiles. Our Vaccine Ontology (VO) analysis classified these 37 vaccines into bacterial, viral, and protozoan vaccine types with different vaccine properties. The ImmPort metadata types were modeled with the Vaccine Investigation Ontology (VIO). Our new ontology-based pipeline extracted vaccine response data from the ImmPort database, annotated them based on ontology, obtained corresponding gene expression data from the GEO, and performed consistent omics data analysis. Our use case found gene profiles shared and differed from live and killed inactivated influenza vaccines. Furthermore, our Omics data analysis using the VIGET tool found that female and male human subjects have differential host responses for influenza vaccines. For example, our study showed much stronger early female responses to influenza vaccination than males, and males was able to show active immune responses at a later stage. Interestingly, the female (but not male) human subject group also showed significantly enriched neutrophil degranulation at Day 3 after influenza vaccination; however, males (but not females) displayed significantly enriched neutrophil degranulation at Day 14 after influenza vaccination. These mechanisms have been used to find differences between the gene lists and pathways of host responses to different vaccines conditional to different factors including vaccine types and host biological sex. Moreover, this framework can be expanded to other vaccines and vaccine categories easily.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3603 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Human Experts and AI in Predicting Autism from Facial Behavior.","authors":"Evangelos Sariyanidi, Casey J Zampella, Ellis DeJardin, John D Herrington, Robert T Schultz, Birkan Tunc","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Advances in computational behavior analysis via artificial intelligence (AI) promise to improve mental healthcare services by providing clinicians with tools to assist diagnosis or measurement of treatment outcomes. This potential has spurred an increasing number of studies in which automated pipelines predict diagnoses of mental health conditions. However, a fundamental question remains unanswered: How do the predictions of the AI algorithms correspond and compare with the predictions of humans? This is a critical question if AI technology is to be used as an assistive tool, because the utility of an AI algorithm would be negligible if it provides little information beyond what clinicians can readily infer. In this paper, we compare the performance of 19 human raters (8 autism experts and 11 non-experts) and that of an AI algorithm in terms of predicting autism diagnosis from short (3-minute) videos of <i>N</i> = 42 participants in a naturalistic conversation. Results show that the AI algorithm achieves an average accuracy of 80.5%, which is comparable to that of clinicians with expertise in autism (83.1%) and clinical research staff without specialized expertise (78.3%). Critically, diagnoses that were inaccurately predicted by most humans (experts and non-experts, alike) were typically correctly predicted by AI. Our results highlight the potential of AI as an assistive tool that can augment clinician diagnostic decision-making.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3359 ITAH","pages":"48-57"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138464759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Extendible Realism-Based Ontology for Kinship.","authors":"Michael Rabenberg, Anuwat Pengput, Werner Ceusters","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Adequately representing kinship relations is crucial for a variety of medical and biomedical applications. Several kinship ontologies have been proposed but none of them have been designed thus far in line with the Basic Formal Ontology. In this paper, we propose a novel kinship ontology that exhibits the following characteristics: (1) it is fully axiomatized in First Order Logic following the rules governing predicate formation as proposed in BFO2020-FOL, (2) it is modularized in 6 separate files written in the Common Logic Interface Format (CLIF) each one of which can be imported based on specific needs, (3) it provides bridging axioms to and from SNOMED CT, and (4) it contains an extra module with axioms which would not be literally true when phrased naively but are crafted in such a way that they highlight the unusual kinship relations they represent and can be used to generate alerts on possible data entry mistakes. We describe design considerations and challenges encountered.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3603 ","pages":"25-35"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141163042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design.","authors":"Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Margo Seltzer","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for <i>prediction</i>, that work does not address <i>policy design</i>, because the algorithms cannot handle weighted data samples. Specifically, they rely on the discreteness of the loss function, which means that real-valued weights cannot be directly used. For example, none of the existing techniques produce policies that incorporate inverse propensity weighting on individual data points. We present three algorithms for efficient sparse weighted decision tree optimization. The first approach directly optimizes the weighted loss function; however, it tends to be computationally inefficient for large datasets. Our second approach, which scales more efficiently, transforms weights to integer values and uses data duplication to transform the weighted decision tree optimization problem into an unweighted (but larger) counterpart. Our third algorithm, which scales to much larger datasets, uses a randomized procedure that samples each data point with a probability proportional to its weight. We present theoretical bounds on the error of the two fast methods and show experimentally that these methods can be two orders of magnitude faster than the direct optimization of the weighted loss, without losing significant accuracy.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3318 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039433/pdf/nihms-1883491.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9197753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design","authors":"Ali Behrouz, Mathias Lécuyer, C. Rudin, M. Seltzer","doi":"10.48550/arXiv.2210.06825","DOIUrl":"https://doi.org/10.48550/arXiv.2210.06825","url":null,"abstract":"Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for prediction, that work does not address policy design, because the algorithms cannot handle weighted data samples. Specifically, they rely on the discreteness of the loss function, which means that real-valued weights cannot be directly used. For example, none of the existing techniques produce policies that incorporate inverse propensity weighting on individual data points. We present three algorithms for efficient sparse weighted decision tree optimization. The first approach directly optimizes the weighted loss function; however, it tends to be computationally inefficient for large datasets. Our second approach, which scales more efficiently, transforms weights to integer values and uses data duplication to transform the weighted decision tree optimization problem into an unweighted (but larger) counterpart. Our third algorithm, which scales to much larger datasets, uses a randomized procedure that samples each data point with a probability proportional to its weight. We present theoretical bounds on the error of the two fast methods and show experimentally that these methods can be two orders of magnitude faster than the direct optimization of the weighted loss, without losing significant accuracy.","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3318 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45358116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Summary Display Ontology: Feedback intervention content, delivery, and interpreted information.","authors":"Zach Landis-Lewis, Cooper Stansbury, John Rincón, Colin Gross","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Feedback loops are vital for decision-making and behavior change in health systems, but not all feedback is of equal value. Clinical performance feedback to healthcare professionals and teams has potential for large effects on clinical practice, but evidence suggests that low-value performance feedback is widespread. A primary barrier to understanding the value of feedback loops in health systems may be a lack of a well-defined model and shared semantics for the information that they carry. An ontology for audit and feedback research may be used to address these issues by standardizing feedback intervention metadata. Research describing feedback interventions recognizes differences between the content of the feedback and its delivery process. However, terms describing feedback intervention content are inconsistent, and appear to vary considerably between audit and feedback frameworks, which can result in confusion around what is being delivered in a performance summary. Our objective was to develop an ontology of a performance summary in a clinical performance feedback intervention for the purposes of standardizing metadata. We developed the Performance Summary Display Ontology (PSDO) iteratively by 1) identifying terms for classes from behavior change theories relating to feedback interventions and cognitive theories of visualization, 2) searching for relevant existing ontologies and classes, and 3) using the terms to specify information content and visual displays in published examples of dashboard displays and feedback reports. PSDO is a lightweight application ontology that specifies performance information content and its representations for the purpose of feedback intervention research and evaluation. PSDO contains 3 primary domains: 1) Performance information content, based on constructs from behavior change theories, 2) Marks and their qualities, based on constructs from visualization theories, and 3) roles that link marks, information content, and other emergent characteristics, as interpreted information. PSDO may enable standardization of metadata for the study of feedback interventions.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3805 ","pages":"L1-L10"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11825144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A community effort for COVID-19 Ontology Harmonization.","authors":"Asiyah Yu Lin, Yuki Yamagata, William D Duncan, Leigh C Carmody, Tatsuya Kushida, Hiroshi Masuya, John Beverley, Biswanath Dutta, Michael DeBellis, Zoë May Pendlington, Paola Roncaglia, Yongqun He","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Ontologies have emerged to become critical to support data and knowledge representation, standardization, integration, and analysis. The SARS-CoV-2 pandemic led to the rapid proliferation of COVID-19 data, as well as the development of many COVID-19 ontologies. In the interest of supporting data interoperability, we initiated a community-based effort to harmonize COVID-19 ontologies. Our effort involves the collaborative discussion among developers of seven COVID-19 related ontologies, and the merging of four ontologies. This effort demonstrates the feasibility of harmonizing these ontologies in an interoperable framework to support integrative representation and analysis of COVID-19 related data and knowledge.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3073 ","pages":"122-127"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10024339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Open-Publishing Response to the COVID-19 Infodemic.","authors":"Halie M Rando, Simina M Boca, Lucy D'Agostino McGowan, Daniel S Himmelstein, Michael P Robson, Vincent Rubinetti, Ryan Velazquez, Casey S Greene, Anthony Gitter","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript's figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"2976 ","pages":"29-38"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9093051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coverage of the Coronavirus Pandemic through Entropy Measures","authors":"V. Soloviev, A. Bielinskyi, N. Kharadzjan","doi":"10.31812/123456789/4427","DOIUrl":"https://doi.org/10.31812/123456789/4427","url":null,"abstract":"The rapidly evolving coronavirus pandemic brings a devastating effect on the entire world and its economy as awhole. Further instability related to COVID-19will negatively affect not only on companies and financial markets, but also on traders and investors that have been interested in saving their investment, minimizing risks, and making decisions such as how to manage their resources, how much to consume and save, when to buy or sell stocks, etc., and these decisions depend on the expectation of when to expect next critical change. Trying to help people in their subsequent decisions, we demonstrate the possibility of constructing indicators of critical and crash phenomena on the example of Bitcoin market crashes for further demonstration of their efficiency on the crash that is related to the coronavirus pandemic. For this purpose, the methods of the theory of complex systems have been used. Since the theory of complex systems has quite an extensive toolkit for exploring the nonlinear complex system, we take a look at the application of the concept of entropy in finance and use this concept to construct 6 effective entropy measures: Shannon entropy, Approximate entropy, Permutation entropy, and 3 Recurrence based entropies. We provide computational results that prove that these indicators could have been used to identify the beginning of the crash and predict the future course of events associated with the current pandemic.","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79338041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges in Realism-Based Ontology Design: a Case Study on Creating an Ontology for Motivational Learning Theories.","authors":"Irshad Ally, Werner Ceusters","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>to identify on the basis of a use case major problem types novices in realism-based ontology design face when attempting to construct an ontology intended to explain differences and commonalities between competing scientific theories.</p><p><strong>Methodology: </strong>an ontology student was tasked (1) to extract manually from a paper about five distinct motivational learning theories the scientific terms used to explain the theories, (2) to map these terms where possible to type-terms from existing realism-based ontologies or create new ones otherwise, (3) to indicate for new type-terms their immediate subsumer, and (4) to document at every step issues that were encountered.</p><p><strong>Results: </strong>where term extraction and type-term assignment were handled satisfactorily, correct classification in function of the BFO was a major challenge. Root causes identified included ambiguous and underspecified term use in the theories, the ontological status of psychological constructs, lack of high quality ontologies for the behavioral sciences and insufficient 'deep' understanding of some BFO entities, in part because of insufficient documentation thereof suitable for learners. The issues the student encountered were often insufficiently described for the instructor to identify the problem without analyzing the source paper itself.</p><p><strong>Conclusion: </strong>whereas behavioral scientists need to do efforts to make their theories comparable, realism-based ontologies can help them therein only when ontology developers and educators put more effort in making them more accessible without violating the principles.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"3073 ","pages":"63-69"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11164408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}