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1. Grounding Realizable Entities

Author

Rabenberg, Michael, Benson, Carter, Donato, Federico, He, Yongqun, Huffman, Anthony, Babcock, Shane, and Beverley, John

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Information Retrieval
Abstract

Ontological representations of qualities, dispositions, and roles have been refined over the past decade, clarifying subtle distinctions in life science research. After articulating a widely-used characterization of these entities within the context of Basic Formal Ontology (BFO), we identify gaps in this treatment and motivate the need for supplementing the BFO characterization. By way of supplement, we propose definitions for grounding relations holding between qualities and dispositions, and dispositions and roles, illustrating our proposal by representing subtle aspects of host-pathogen interactions., Comment: 13

Published
2024

2. Credentials in the Occupation Ontology

Author

Beverley, John, McGill, Robin, Smith, Sam, Zheng, Jie, De Colle, Giacomo, Wilson, Finn, Diller, Matthew, Duncan, William D., Hogan, William R., and He, Yongqun

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Information Retrieval
Abstract

The term credential encompasses educational certificates, degrees, certifications, and government-issued licenses. An occupational credential is a verification of an individuals qualification or competence issued by a third party with relevant authority. Job seekers often leverage such credentials as evidence that desired qualifications are satisfied by their holders. Many U.S. education and workforce development organizations have recognized the importance of credentials for employment and the challenges of understanding the value of credentials. In this study, we identified and ontologically defined credential and credential-related terms at the textual and semantic levels based on the Occupation Ontology (OccO), a BFO-based ontology. Different credential types and their authorization logic are modeled. We additionally defined a high-level hierarchy of credential related terms and relations among many terms, which were initiated in concert with the Alabama Talent Triad (ATT) program, which aims to connect learners, earners, employers and education/training providers through credentials and skills. To our knowledge, our research provides for the first time systematic ontological modeling of the important domain of credentials and related contents, supporting enhanced credential data and knowledge integration in the future., Comment: 11

Published
2024

3. Killing tumor-associated bacteria with a liposomal antibiotic generates neoantigens that induce anti-tumor immune responses

Author

Wang, Menglin, Rousseau, Benoit, Qiu, Kunyu, Huang, Guannan, Zhang, Yu, Su, Hang, Le Bihan-Benjamin, Christine, Khati, Ines, Artz, Oliver, Foote, Michael B., Cheng, Yung-Yi, Lee, Kuo-Hsiung, Miao, Michael Z., Sun, Yue, Bousquet, Philippe-Jean, Hilmi, Marc, Dumas, Elise, Hamy, Anne-Sophie, Reyal, Fabien, Lin, Lin, Armistead, Paul M., Song, Wantong, Vargason, Ava, Arthur, Janelle C., Liu, Yun, Guo, Jianfeng, Zhou, Xuefei, Nguyen, Juliane, He, Yongqun, Ting, Jenny P.-Y., Anselmo, Aaron C., and Huang, Leaf

Published
2024
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4. The Human Phenotype Ontology in 2024: phenotypes around the world

Author

Gargano, Michael A, Matentzoglu, Nicolas, Coleman, Ben, Addo-Lartey, Eunice B, Anagnostopoulos, Anna V, Anderton, Joel, Avillach, Paul, Bagley, Anita M, Bakštein, Eduard, Balhoff, James P, Baynam, Gareth, Bello, Susan M, Berk, Michael, Bertram, Holli, Bishop, Somer, Blau, Hannah, Bodenstein, David F, Botas, Pablo, Boztug, Kaan, Čady, Jolana, Callahan, Tiffany J, Cameron, Rhiannon, Carbon, Seth J, Castellanos, Francisco, Caufield, J Harry, Chan, Lauren E, Chute, Christopher G, Cruz-Rojo, Jaime, Dahan-Oliel, Noémi, Davids, Jon R, de Dieuleveult, Maud, de Souza, Vinicius, de Vries, Bert BA, de Vries, Esther, DePaulo, J Raymond, Derfalvi, Beata, Dhombres, Ferdinand, Diaz-Byrd, Claudia, Dingemans, Alexander JM, Donadille, Bruno, Duyzend, Michael, Elfeky, Reem, Essaid, Shahim, Fabrizzi, Carolina, Fico, Giovanna, Firth, Helen V, Freudenberg-Hua, Yun, Fullerton, Janice M, Gabriel, Davera L, Gilmour, Kimberly, Giordano, Jessica, Goes, Fernando S, Moses, Rachel Gore, Green, Ian, Griese, Matthias, Groza, Tudor, Gu, Weihong, Guthrie, Julia, Gyori, Benjamin, Hamosh, Ada, Hanauer, Marc, Hanušová, Kateřina, He, Yongqun, Hegde, Harshad, Helbig, Ingo, Holasová, Kateřina, Hoyt, Charles Tapley, Huang, Shangzhi, Hurwitz, Eric, Jacobsen, Julius OB, Jiang, Xiaofeng, Joseph, Lisa, Keramatian, Kamyar, King, Bryan, Knoflach, Katrin, Koolen, David A, Kraus, Megan L, Kroll, Carlo, Kusters, Maaike, Ladewig, Markus S, Lagorce, David, Lai, Meng-Chuan, Lapunzina, Pablo, Laraway, Bryan, Lewis-Smith, David, Li, Xiarong, Lucano, Caterina, Majd, Marzieh, Marazita, Mary L, Martinez-Glez, Victor, McHenry, Toby H, McInnis, Melvin G, McMurry, Julie A, Mihulová, Michaela, Millett, Caitlin E, Mitchell, Philip B, Moslerová, Veronika, Narutomi, Kenji, Nematollahi, Shahrzad, and Nevado, Julian

Subjects
Biological Sciences, Genetics, Networking and Information Technology R&D (NITRD), Human Genome, Machine Learning and Artificial Intelligence, Good Health and Well Being, Humans, Biological Ontologies, Phenotype, Genomics, Algorithms, Rare Diseases, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Published
2024

9. Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text

Author

Rehana, Hasin, Çam, Nur Bengisu, Basmaci, Mert, Zheng, Jie, Jemiyo, Christianah, He, Yongqun, Özgür, Arzucan, and Hur, Junguk

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedical literature, there is a growing need for automated and accurate extraction of PPIs to facilitate scientific knowledge discovery. Pre-trained language models, such as generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT), have shown promising results in natural language processing (NLP) tasks. We evaluated the performance of PPI identification of multiple GPT and BERT models using three manually curated gold-standard corpora: Learning Language in Logic (LLL) with 164 PPIs in 77 sentences, Human Protein Reference Database with 163 PPIs in 145 sentences, and Interaction Extraction Performance Assessment with 335 PPIs in 486 sentences. BERT-based models achieved the best overall performance, with BioBERT achieving the highest recall (91.95%) and F1-score (86.84%) and PubMedBERT achieving the highest precision (85.25%). Interestingly, despite not being explicitly trained for biomedical texts, GPT-4 achieved commendable performance, comparable to the top-performing BERT models. It achieved a precision of 88.37%, a recall of 85.14%, and an F1-score of 86.49% on the LLL dataset. These results suggest that GPT models can effectively detect PPIs from text data, offering promising avenues for application in biomedical literature mining. Further research could explore how these models might be fine-tuned for even more specialized tasks within the biomedical domain.

Published
2023

10. A reference tissue atlas for the human kidney

Author

Hansen, Jens, Sealfon, Rachel, Menon, Rajasree, Eadon, Michael T, Lake, Blue B, Steck, Becky, Anjani, Kavya, Parikh, Samir, Sigdel, Tara K, Zhang, Guanshi, Velickovic, Dusan, Barwinska, Daria, Alexandrov, Theodore, Dobi, Dejan, Rashmi, Priyanka, Otto, Edgar A, Rivera, Miguel, Rose, Michael P, Anderton, Christopher R, Shapiro, John P, Pamreddy, Annapurna, Winfree, Seth, Xiong, Yuguang, He, Yongqun, de Boer, Ian H, Hodgin, Jeffrey B, Barisoni, Laura, Naik, Abhijit S, Sharma, Kumar, Sarwal, Minnie M, Zhang, Kun, Himmelfarb, Jonathan, Rovin, Brad, El-Achkar, Tarek M, Laszik, Zoltan, He, John Cijiang, Dagher, Pierre C, Valerius, M Todd, Jain, Sanjay, Satlin, Lisa M, Troyanskaya, Olga G, Kretzler, Matthias, Iyengar, Ravi, Azeloglu, Evren U, and Project, Kidney Precision Medicine

Subjects
Biotechnology, Genetics, Kidney Disease, Aetiology, 2.1 Biological and endogenous factors, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Renal and urogenital, Good Health and Well Being, Humans, Kidney, Kidney Diseases, Metabolomics, Proteomics, Transcriptome, Kidney Precision Medicine Project
Abstract

Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

Published
2022

11. An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)

Author

Liu, Sijia, Wen, Andrew, Wang, Liwei, He, Huan, Fu, Sunyang, Miller, Robert, Williams, Andrew, Harris, Daniel, Kavuluru, Ramakanth, Liu, Mei, Abu-el-rub, Noor, Schutte, Dalton, Zhang, Rui, Rouhizadeh, Masoud, Osborne, John D., He, Yongqun, Topaloglu, Umit, Hong, Stephanie S, Saltz, Joel H, Schaffter, Thomas, Pfaff, Emily, Chute, Christopher G., Duong, Tim, Haendel, Melissa A., Fuentes, Rafael, Szolovits, Peter, Xu, Hua, Liu, Hongfang, Collaborative, National COVID Cohort, Processing, Natural Language, and Subgroup

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The corpora were derived from texts from three different institutions (Mayo Clinic, University of Kentucky, University of Minnesota). The gold standard annotations were tested with a single institution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study and adoption. Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP., Comment: update on contents

Published
2021

13. Ontology-based annotation and analysis of COVID-19 phenotypes

Author

Wang, Yang, Zhang, Fengwei, Yu, Hong, Ye, Xianwei, and He, Yongqun

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

The epidemic of COVID-19 has caused an unpredictable and devastated disaster to the public health in different territories around the world. Common phenotypes include fever, cough, shortness of breath, and chills. With more cases investigated, other clinical phenotypes are gradually recognized, for example, loss of smell, and loss of tastes. Compared with discharged or cured patients, severe or died patients often have one or more comorbidities, such as hypertension, diabetes, and cardiovascular disease. In this study, we systematically collected and analyzed COVID-19-related clinical phenotypes from 70 articles. The commonly occurring 17 phenotypes were classified into different groups based on the Human Phenotype Ontology (HPO). Based on the HP classification, we systematically analyze three nervous phenotypes (loss of smell, loss of taste, and headache) and four abdominal phenotypes (nausea, vomiting, abdominal pain, and diarrhea) identified in patients, and found that patients from Europe and USA turned to have higher nervous phenotypes and abdominal phenotypes than patients from Asia. A total of 23 comorbidities were found to commonly exist among COVID-19 patients. Patients with these comorbidities such as diabetes and kidney failure had worse outcomes compared with those without these comorbidities.

Published
2020

14. Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19

Author

Yu, Hong, Li, Li, Huang, Hsin-hui, Wang, Yang, Liu, Yingtong, Ong, Edison, Huffman, Anthony, Zeng, Tao, Zhang, Jingsong, Li, Pengpai, Liu, Zhiping, Zhang, Xiangyan, Ye, Xianwei, Handelman, Samuel K., Higgins, Gerry, Omenn, Gilbert S., Athey, Brian, Hur, Junguk, Chen, Luonan, and He, Yongqun

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

Given the existing COVID-19 pandemic worldwide, it is critical to systematically study the interactions between hosts and coronaviruses including SARS-Cov, MERS-Cov, and SARS-CoV-2 (cause of COVID-19). We first created four host-pathogen interaction (HPI)-Outcome postulates, and generated a HPI-Outcome model as the basis for understanding host-coronavirus interactions (HCI) and their relations with the disease outcomes. We hypothesized that ontology can be used as an integrative platform to classify and analyze HCI and disease outcomes. Accordingly, we annotated and categorized different coronaviruses, hosts, and phenotypes using ontologies and identified their relations. Various COVID-19 phenotypes are hypothesized to be caused by the backend HCI mechanisms. To further identify the causal HCI-outcome relations, we collected 35 experimentally-verified HCI protein-protein interactions (PPIs), and applied literature mining to identify additional host PPIs in response to coronavirus infections. The results were formulated in a logical ontology representation for integrative HCI-outcome understanding. Using known PPIs as baits, we also developed and applied a domain-inferred prediction method to predict new PPIs and identified their pathological targets on multiple organs. Overall, our proposed ontology-based integrative framework combined with computational predictions can be used to support fundamental understanding of the intricate interactions between human patients and coronaviruses (including SARS-CoV-2) and their association with various disease outcomes., Comment: 32 pages, 1 table, 6 figures

Published
2020

15. Standardization of assay representation in the Ontology for Biomedical Investigations

Author

Vita, Randi, Zheng, Jie, Jackson, Rebecca, Dooley, Damion, Overton, James A, Miller, Mark A, Berrios, Daniel C, Scheuermann, Richard H, He, Yongqun, McGinty, Hande Küçük, Brochhausen, Mathias, Lin, Aisyah Yu, Jain, Sagar B, Chibucos, Marcus C, Judkins, John, Giglio, Michelle G, Feng, Irene Y, Burns, Gully, Brush, Matthew H, Peters, Bjoern, and Stoeckert, Christian J

Subjects
Data Management and Data Science, Information and Computing Sciences, Biological Sciences, Bioinformatics and Computational Biology, Biological Ontologies, Language, Reference Standards, Data Format, Library and Information Studies, Bioinformatics and computational biology, Data management and data science
Abstract

The Ontology for Biomedical Investigations (OBI) underwent a focused review of assay term annotations, logic and hierarchy with a goal to improve and standardize these terms. As a result, inconsistencies in W3C Web Ontology Language (OWL) expressions were identified and corrected, and additionally, standardized design patterns and a formalized template to maintain them were developed. We describe here this informative and productive process to describe the specific benefits and obstacles for OBI and the universal lessons for similar projects.

Published
2021

16. Rationale and design of the Kidney Precision Medicine Project

Author

de Boer, Ian H, Alpers, Charles E, Azeloglu, Evren U, Balis, Ulysses GJ, Barasch, Jonathan M, Barisoni, Laura, Blank, Kristina N, Bomback, Andrew S, Brown, Keith, Dagher, Pierre C, Dighe, Ashveena L, Eadon, Michael T, El-Achkar, Tarek M, Gaut, Joseph P, Hacohen, Nir, He, Yongqun, Hodgin, Jeffrey B, Jain, Sanjay, Kellum, John A, Kiryluk, Krzysztof, Knight, Richard, Laszik, Zoltan G, Lienczewski, Chrysta, Mariani, Laura H, McClelland, Robyn L, Menez, Steven, Moledina, Dennis G, Mooney, Sean D, O’Toole, John F, Palevsky, Paul M, Parikh, Chirag R, Poggio, Emilio D, Rosas, Sylvia E, Rosengart, Matthew R, Sarwal, Minnie M, Schaub, Jennifer A, Sedor, John R, Sharma, Kumar, Steck, Becky, Toto, Robert D, Troyanskaya, Olga G, Tuttle, Katherine R, Vazquez, Miguel A, Waikar, Sushrut S, Williams, Kayleen, Wilson, Francis Perry, Zhang, Kun, Iyengar, Ravi, Kretzler, Matthias, Himmelfarb, Jonathan, Project, Kidney Precision Medicine, Lecker, Stewart, Stillman, Isaac, Waikar, Sushrut, Mcmahon, Gearoid, Weins, Astrid, Short, Samuel, Hoover, Paul, Aulisio, Mark, Cooperman, Leslie, Herlitz, Leal, O’Toole, John, Poggio, Emilio, Sedor, John, Jolly, Stacey, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Bomback, Andrew, Canetta, Pietro A, d’Agati, Vivette D, Kudose, Satoru, Mehl, Karla, Radhakrishnan, Jai, Weng, Chenhua, Alexandrov, Theodore, Ashkar, Tarek, Barwinska, Daria, Dagher, Pierre, Dunn, Kenneth, Eadon, Michael, Ferkowicz, Michael, Kelly, Katherine, Sutton, Timothy, Winfree, Seth, Parikh, Chirag, Rosenberg, Avi, Villalobos, Pam, Malik, Rubab, Fine, Derek, Atta, Mohammed, Trujillo, Jose Manuel Monroy, Slack, Alison, Rosas, Sylvia, and Williams, Mark

Subjects
Clinical Research, Transplantation, Kidney Disease, Prevention, Renal and urogenital, Good Health and Well Being, Acute Kidney Injury, Adult, Humans, Kidney, Precision Medicine, Prospective Studies, Proteomics, Renal Insufficiency, Chronic, acute kidney injury, chronic kidney disease, diabetes, hypertension, precision medicine, Kidney Precision Medicine Project, Clinical Sciences, Urology & Nephrology
Abstract

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

Published
2021

17. The Human Phenotype Ontology in 2021

Author

Köhler, Sebastian, Gargano, Michael, Matentzoglu, Nicolas, Carmody, Leigh C, Lewis-Smith, David, Vasilevsky, Nicole A, Danis, Daniel, Balagura, Ganna, Baynam, Gareth, Brower, Amy M, Callahan, Tiffany J, Chute, Christopher G, Est, Johanna L, Galer, Peter D, Ganesan, Shiva, Griese, Matthias, Haimel, Matthias, Pazmandi, Julia, Hanauer, Marc, Harris, Nomi L, Hartnett, Michael J, Hastreiter, Maximilian, Hauck, Fabian, He, Yongqun, Jeske, Tim, Kearney, Hugh, Kindle, Gerhard, Klein, Christoph, Knoflach, Katrin, Krause, Roland, Lagorce, David, McMurry, Julie A, Miller, Jillian A, Munoz-Torres, Monica C, Peters, Rebecca L, Rapp, Christina K, Rath, Ana M, Rind, Shahmir A, Rosenberg, Avi Z, Segal, Michael M, Seidel, Markus G, Smedley, Damian, Talmy, Tomer, Thomas, Yarlalu, Wiafe, Samuel A, Xian, Julie, Yüksel, Zafer, Helbig, Ingo, Mungall, Christopher J, Haendel, Melissa A, and Robinson, Peter N

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Neurosciences, Neurodegenerative, Networking and Information Technology R&D (NITRD), Animals, Biological Ontologies, Computational Biology, Databases, Factual, Disease, Disease Models, Animal, Genome, Genotype, Humans, Infant, Newborn, International Cooperation, Internet, Neonatal Screening, Pharmacogenetics, Phenotype, Software, Terminology as Topic, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

Published
2021

18. OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies

Author

Yang, Ying, Wybrow, Michael, Li, Yuan-Fang, Czauderna, Tobias, and He, Yongqun

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence
Abstract

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Prot{\'e}g{\'e}. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts., Comment: Accepted at IEEE InfoVis 2019

Published
2019
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19. Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

Author

Ong, Edison, Wang, Lucy L, Schaub, Jennifer, O’Toole, John F, Steck, Becky, Rosenberg, Avi Z, Dowd, Frederick, Hansen, Jens, Barisoni, Laura, Jain, Sanjay, de Boer, Ian H, Valerius, M Todd, Waikar, Sushrut S, Park, Christopher, Crawford, Dana C, Alexandrov, Theodore, Anderton, Christopher R, Stoeckert, Christian, Weng, Chunhua, Diehl, Alexander D, Mungall, Christopher J, Haendel, Melissa, Robinson, Peter N, Himmelfarb, Jonathan, Iyengar, Ravi, Kretzler, Matthias, Mooney, Sean, and He, Yongqun

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Kidney Disease, Networking and Information Technology R&D (NITRD), Generic health relevance, Renal and urogenital, Good Health and Well Being, Atlases as Topic, Big Data, Biological Ontologies, Humans, Kidney Diseases, Phenotype, Precision Medicine, Kidney Precision Medicine Project, Urology & Nephrology, Clinical sciences
Abstract

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

Published
2020

20. Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative

Author

Yoo, Yun J., Wilkins, Kenneth J., Alakwaa, Fadhl, Liu, Feifan, Torre-Healy, Luke A., Krichevsky, Spencer, Hong, Stephanie S., Sakhuja, Ankit, Potu, Chetan K., Saltz, Joel H., Saran, Rajiv, Zhu, Richard L., Setoguchi, Soko, Kane-Gill, Sandra L., Mallipattu, Sandeep K., He, Yongqun, Ellison, David H., Byrd, James B., Parikh, Chirag R., Moffitt, Richard A., and Koraishy, Farrukh M.

Published
2023
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22. A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

Author

He, Yongqun, Yu, Hong, Huffman, Anthony, Lin, Asiyah Yu, Natale, Darren A., Beverley, John, Zheng, Ling, Perl, Yehoshua, Wang, Zhigang, Liu, Yingtong, Ong, Edison, Wang, Yang, Huang, Philip, Tran, Long, Du, Jinyang, Shah, Zalan, Shah, Easheta, Desai, Roshan, Huang, Hsin-hui, Tian, Yujia, Merrell, Eric, Duncan, William D., Arabandi, Sivaram, Schriml, Lynn M., Zheng, Jie, Masci, Anna Maria, Wang, Liwei, Liu, Hongfang, Smaili, Fatima Zohra, Hoehndorf, Robert, Pendlington, Zoë May, Roncaglia, Paola, Ye, Xianwei, Xie, Jiangan, Tang, Yi-Wei, Yang, Xiaolin, Peng, Suyuan, Zhang, Luxia, Chen, Luonan, Hur, Junguk, Omenn, Gilbert S., Athey, Brian, and Smith, Barry

Published
2022
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28. Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration

Author

Ong, Edison, Xiang, Zuoshuang, Zhao, Bin, Liu, Yue, Lin, Yu, Zheng, Jie, Mungall, Chris, Courtot, Mélanie, Ruttenberg, Alan, and He, Yongqun

Subjects
Networking and Information Technology R&D (NITRD), Biological Ontologies, Databases, Factual, Software, Web Browser, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Developmental Biology
Abstract

Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies.

Published
2017

29. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability

Author

Diehl, Alexander D, Meehan, Terrence F, Bradford, Yvonne M, Brush, Matthew H, Dahdul, Wasila M, Dougall, David S, He, Yongqun, Osumi-Sutherland, David, Ruttenberg, Alan, Sarntivijai, Sirarat, Van Slyke, Ceri E, Vasilevsky, Nicole A, Haendel, Melissa A, Blake, Judith A, and Mungall, Christopher J

Subjects
Information and Computing Sciences, Artificial Intelligence, Networking and Information Technology R&D (NITRD), Genetics, Biological Ontologies, Cells, Natural Language Processing, Nervous System, Other Biological Sciences, Artificial Intelligence and Image Processing, Information Systems, Information and computing sciences
Abstract

BackgroundThe Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies.Construction and contentRecent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning.Utility and discussionThe CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs.ConclusionsThe ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.

Published
2016

32. Coordinating virus research: The Virus Infectious Disease Ontology

Author

Beverley, John, primary, Babcock, Shane, additional, Carvalho, Gustavo, additional, Cowell, Lindsay G., additional, Duesing, Sebastian, additional, He, Yongqun, additional, Hurley, Regina, additional, Merrell, Eric, additional, Scheuermann, Richard H., additional, and Smith, Barry, additional

Published
2024
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33. CanVaxKB: a web-based cancer vaccine knowledgebase

Author

Asfaw, Eliyas, primary, Lin, Asiyah Yu, additional, Huffman, Anthony, additional, Li, Siqi, additional, George, Madison, additional, Darancou, Chloe, additional, Kalter, Madison, additional, Wehbi, Nader, additional, Bartels, Davis, additional, Fleck, Elyse, additional, Tran, Nancy, additional, Faghihnia, Daniel, additional, Berke, Kimberly, additional, Sutariya, Ronak, additional, Reyal, Farah, additional, Tammam, Youssef, additional, Zhao, Bin, additional, Ong, Edison, additional, Xiang, Zuoshuang, additional, He, Virginia, additional, Song, Justin, additional, Seleznev, Andrey I, additional, Guo, Jinjing, additional, Pan, Yuanyi, additional, Zheng, Jie, additional, and He, Yongqun, additional

Published
2024
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38. HINO: a BFO-aligned ontology representing human molecular interactions and pathways

Author

He, Yongqun and Xiang, Zoushuang

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases, Quantitative Biology - Molecular Networks
Abstract

Many database resources, such as Reactome, collect manually annotated reactions, interactions, and pathways from peer-reviewed publications. The interactors (e.g., a protein), interactions, and pathways in these data resources are often represented as instances in using BioPAX, a standard pathway data exchange format. However, these interactions are better represented as classes (or universals) since they always occur given appropriate conditions. This study aims to represent various human interaction pathways and networks as classes via a formal ontology aligned with the Basic Formal Ontology (BFO). Towards this goal, the Human Interaction Network Ontology (HINO) was generated by extending the BFO-aligned Interaction Network Ontology (INO). All human pathways and associated processes and interactors listed in Reactome and represented in BioPAX were first converted to ontology classes by aligning them under INO. Related terms and associated relations and hierarchies from external ontologies (e.g., CHEBI and GO) were also retrieved and imported into HINO. HINO ontology terms were resolved in the linked ontology data server Ontobee. The RDF triples stored in the RDF triple store are queryable through a SPARQL program. Such an ontology system supports advanced pathway data integration and applications., Comment: 7 pages, 5 figures

Published
2013

40. The Human Phenotype Ontology in 2024: phenotypes around the world

Author

Gargano, Michael A, primary, Matentzoglu, Nicolas, additional, Coleman, Ben, additional, Addo-Lartey, Eunice B, additional, Anagnostopoulos, Anna V, additional, Anderton, Joel, additional, Avillach, Paul, additional, Bagley, Anita M, additional, Bakštein, Eduard, additional, Balhoff, James P, additional, Baynam, Gareth, additional, Bello, Susan M, additional, Berk, Michael, additional, Bertram, Holli, additional, Bishop, Somer, additional, Blau, Hannah, additional, Bodenstein, David F, additional, Botas, Pablo, additional, Boztug, Kaan, additional, Čady, Jolana, additional, Callahan, Tiffany J, additional, Cameron, Rhiannon, additional, Carbon, Seth J, additional, Castellanos, Francisco, additional, Caufield, J Harry, additional, Chan, Lauren E, additional, Chute, Christopher G, additional, Cruz-Rojo, Jaime, additional, Dahan-Oliel, Noémi, additional, Davids, Jon R, additional, de Dieuleveult, Maud, additional, de Souza, Vinicius, additional, de Vries, Bert B A, additional, de Vries, Esther, additional, DePaulo, J Raymond, additional, Derfalvi, Beata, additional, Dhombres, Ferdinand, additional, Diaz-Byrd, Claudia, additional, Dingemans, Alexander J M, additional, Donadille, Bruno, additional, Duyzend, Michael, additional, Elfeky, Reem, additional, Essaid, Shahim, additional, Fabrizzi, Carolina, additional, Fico, Giovanna, additional, Firth, Helen V, additional, Freudenberg-Hua, Yun, additional, Fullerton, Janice M, additional, Gabriel, Davera L, additional, Gilmour, Kimberly, additional, Giordano, Jessica, additional, Goes, Fernando S, additional, Moses, Rachel Gore, additional, Green, Ian, additional, Griese, Matthias, additional, Groza, Tudor, additional, Gu, Weihong, additional, Guthrie, Julia, additional, Gyori, Benjamin, additional, Hamosh, Ada, additional, Hanauer, Marc, additional, Hanušová, Kateřina, additional, He, Yongqun (Oliver), additional, Hegde, Harshad, additional, Helbig, Ingo, additional, Holasová, Kateřina, additional, Hoyt, Charles Tapley, additional, Huang, Shangzhi, additional, Hurwitz, Eric, additional, Jacobsen, Julius O B, additional, Jiang, Xiaofeng, additional, Joseph, Lisa, additional, Keramatian, Kamyar, additional, King, Bryan, additional, Knoflach, Katrin, additional, Koolen, David A, additional, Kraus, Megan L, additional, Kroll, Carlo, additional, Kusters, Maaike, additional, Ladewig, Markus S, additional, Lagorce, David, additional, Lai, Meng-Chuan, additional, Lapunzina, Pablo, additional, Laraway, Bryan, additional, Lewis-Smith, David, additional, Li, Xiarong, additional, Lucano, Caterina, additional, Majd, Marzieh, additional, Marazita, Mary L, additional, Martinez-Glez, Victor, additional, McHenry, Toby H, additional, McInnis, Melvin G, additional, McMurry, Julie A, additional, Mihulová, Michaela, additional, Millett, Caitlin E, additional, Mitchell, Philip B, additional, Moslerová, Veronika, additional, Narutomi, Kenji, additional, Nematollahi, Shahrzad, additional, Nevado, Julian, additional, Nierenberg, Andrew A, additional, Čajbiková, Nikola Novák, additional, Nurnberger, John I, additional, Ogishima, Soichi, additional, Olson, Daniel, additional, Ortiz, Abigail, additional, Pachajoa, Harry, additional, Perez de Nanclares, Guiomar, additional, Peters, Amy, additional, Putman, Tim, additional, Rapp, Christina K, additional, Rath, Ana, additional, Reese, Justin, additional, Rekerle, Lauren, additional, Roberts, Angharad M, additional, Roy, Suzy, additional, Sanders, Stephan J, additional, Schuetz, Catharina, additional, Schulte, Eva C, additional, Schulze, Thomas G, additional, Schwarz, Martin, additional, Scott, Katie, additional, Seelow, Dominik, additional, Seitz, Berthold, additional, Shen, Yiping, additional, Similuk, Morgan N, additional, Simon, Eric S, additional, Singh, Balwinder, additional, Smedley, Damian, additional, Smith, Cynthia L, additional, Smolinsky, Jake T, additional, Sperry, Sarah, additional, Stafford, Elizabeth, additional, Stefancsik, Ray, additional, Steinhaus, Robin, additional, Strawbridge, Rebecca, additional, Sundaramurthi, Jagadish Chandrabose, additional, Talapova, Polina, additional, Tenorio Castano, Jair A, additional, Tesner, Pavel, additional, Thomas, Rhys H, additional, Thurm, Audrey, additional, Turnovec, Marek, additional, van Gijn, Marielle E, additional, Vasilevsky, Nicole A, additional, Vlčková, Markéta, additional, Walden, Anita, additional, Wang, Kai, additional, Wapner, Ron, additional, Ware, James S, additional, Wiafe, Addo A, additional, Wiafe, Samuel A, additional, Wiggins, Lisa D, additional, Williams, Andrew E, additional, Wu, Chen, additional, Wyrwoll, Margot J, additional, Xiong, Hui, additional, Yalin, Nefize, additional, Yamamoto, Yasunori, additional, Yatham, Lakshmi N, additional, Yocum, Anastasia K, additional, Young, Allan H, additional, Yüksel, Zafer, additional, Zandi, Peter P, additional, Zankl, Andreas, additional, Zarante, Ignacio, additional, Zvolský, Miroslav, additional, Toro, Sabrina, additional, Carmody, Leigh C, additional, Harris, Nomi L, additional, Munoz-Torres, Monica C, additional, Danis, Daniel, additional, Mungall, Christopher J, additional, Köhler, Sebastian, additional, Haendel, Melissa A, additional, and Robinson, Peter N, additional

Published
2023
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42. Modeling biomedical experimental processes with OBI

Author

Brinkman, Ryan R, Courtot, Mélanie, Derom, Dirk, Fostel, Jennifer M, He, Yongqun, Lord, Phillip, Malone, James, Parkinson, Helen, Peters, Bjoern, Rocca-Serra, Philippe, Ruttenberg, Alan, Sansone, Susanna-Assunta, Soldatova, Larisa N, Stoeckert, Christian J, Turner, Jessica A, Zheng, Jie, and obi-users@googlegroups.com

Abstract

Abstract Background Experimental descriptions are typically stored as free text without using standardized terminology, creating challenges in comparison, reproduction and analysis. These difficulties impose limitations on data exchange and information retrieval. Results The Ontology for Biomedical Investigations (OBI), developed as a global, cross-community effort, provides a resource that represents biomedical investigations in an explicit and integrative framework. Here we detail three real-world applications of OBI, provide detailed modeling information and explain how to use OBI. Conclusion We demonstrate how OBI can be applied to different biomedical investigations to both facilitate interpretation of the experimental process and increase the computational processing and integration within the Semantic Web. The logical definitions of the entities involved allow computers to unambiguously understand and integrate different biological experimental processes and their relevant components. Availability OBI is available at http://purl.obolibrary.org/obo/obi/2009-11-02/obi.owl

Published
2010

43. An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)

Author

Liu, Sijia, primary, Wen, Andrew, additional, Wang, Liwei, additional, He, Huan, additional, Fu, Sunyang, additional, Miller, Robert, additional, Williams, Andrew, additional, Harris, Daniel, additional, Kavuluru, Ramakanth, additional, Liu, Mei, additional, Abu-el-Rub, Noor, additional, Schutte, Dalton, additional, Zhang, Rui, additional, Rouhizadeh, Masoud, additional, Osborne, John D, additional, He, Yongqun, additional, Topaloglu, Umit, additional, Hong, Stephanie S, additional, Saltz, Joel H, additional, Schaffter, Thomas, additional, Pfaff, Emily, additional, Chute, Christopher G, additional, Duong, Tim, additional, Haendel, Melissa A, additional, Fuentes, Rafael, additional, Szolovits, Peter, additional, Xu, Hua, additional, and Liu, Hongfang, additional

Published
2023
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