Relevant bibliographies by topics / Biological ontology

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Biological ontology'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Biological ontology"

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Dupré, John, and Maureen A. O’Malley. "Metagenomics and biological ontology." Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 38, no. 4 (2007): 834–46. http://dx.doi.org/10.1016/j.shpsc.2007.09.001.

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Schulz, S., H. Stenzhorn, and M. Boeker. "The ontology of biological taxa." Bioinformatics 24, no. 13 (2008): i313—i321. http://dx.doi.org/10.1093/bioinformatics/btn158.

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Hoehndorf, Robert, Frank Loebe, Roberto Poli, Heinrich Herre, and Janet Kelso. "GFO-Bio: A biological core ontology." Applied Ontology 3, no. 4 (2008): 219–27. http://dx.doi.org/10.3233/ao-2008-0055.

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Menon, Vasudev Ramdas. "On the Ontology of Biological Entities." Resonance 29, no. 8 (2024): 1111–26. http://dx.doi.org/10.1007/s12045-024-1111-4.

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Menon, Vasudev Ramdas. "On the Ontology of Biological Entities." Resonance 29, no. 9 (2024): 1231–53. http://dx.doi.org/10.1007/s12045-024-1231-x.

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Diehl, A. D., J. A. Lee, R. H. Scheuermann, and J. A. Blake. "Ontology development for biological systems: immunology." Bioinformatics 23, no. 7 (2007): 913–15. http://dx.doi.org/10.1093/bioinformatics/btm029.

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Kment, A. "On the ontology of biological aging." Archives of Gerontology and Geriatrics 7, no. 3 (1988): 221–27. http://dx.doi.org/10.1016/0167-4943(88)90005-2.

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Buttigieg, Pier Luigi, Norman Morrison, Barry Smith, Christopher J. Mungall, and Suzanna E. Lewis. "The environment ontology: contextualising biological and biomedical entities." JOURNAL OF BIOMEDICAL SEMANTICS 4, no. 2013 (2013): 43. https://doi.org/10.1186/2041-1480-4-43.

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As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO; www.environmentontology.org) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summar
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Meenakshi, Sundaram A., Ali Anooja, S. Patil S, and A. Ajil. "Bi-Cluster Based Analysis on Gene Ontology." MileStone Transactions on Medical Technometrics 1, no. 1 (2023): 10–17. https://doi.org/10.5281/zenodo.8073114.

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Understanding biological activity requires the detection of crucial proteins. The identification of significant genes throughout the entire genome is advantageous for a number of reasons, including the categorization of critical genes for health and sickness, the rational creation of drugs, etc. Statistical methods have been suggested for predicting essential or requisite proteins/gene/GO terms, employed in protein networks.  The computational approaches focusing on the topological characteristics or centrality approaches ignore the biologically relevant intrinsic features of essenti
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Trappes, Rose. "Evaluating Elizabeth Grosz's Biological Turn." Hypatia 34, no. 4 (2019): 736–54. http://dx.doi.org/10.1111/hypa.12487.

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Elizabeth Grosz's interpretation of Darwinian evolutionary theory to ground a feminist ontology of biology has been particularly controversial. Most critics have understood Grosz as supporting her theory with empirical evidence, and they criticize her for being either inaccurate or uncritical of and overly dependent on science. I argue that Grosz reads Darwin as a philosopher in a Deleuzian and Irigarayan sense, and that Grosz's project is therefore better understood in terms of its ethical and political goals rather than in terms of empirical adequacy. Employing this evaluative framework lead
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Dissertations / Theses on the topic "Biological ontology"

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Bartol, Jordan Nelson. "'Kind Historicism' & biological ontology." Thesis, University of Leeds, 2015. http://etheses.whiterose.ac.uk/9473/.

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This thesis develops a new theory of natural kinds for the biological world, called ‘Kind Historicism’, and addresses the relationship between natural kind theorizing and scientific reasoning. Applied to natural kinds and individuals in biology, Kind Historicism provides an ontology of the biological world. Discussions of biological ontology have struggled to balance insights from scientific practice with tools from analytic philosophy, metaphysics, and ontology. Ontological questions and practical/epistemic questions are often entangled. This thesis separates the two enquires, explaining why
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Mossley, David John. "Biological being : philosophical issues in scientific realism, experiments and (dis)unity." Thesis, Durham University, 1997. http://etheses.dur.ac.uk/4754/.

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The biological sciences are changing the ways in which we understand ourselves Biological Being is a philosophical exploration of biology, mapping some of the features of the field that make it so important in generating these changes Two central themes are at the heart of this exploration: biology is a science that should be grasped from a realist position, and it is a science that reveals a disunified, pluralistic world of kinds of things. After an introduction of some the issues involved, in three substantial chapters these themes are unpacked and analysed. The first major chapter is about
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Davidson, Christopher. "Applying database and ontology design techniques to a NASA biological research repository." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000922.

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Feltrin, Erika. "Contribution to OBO ontologies and application of structured vocabularies for data integration and biological reasoning." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3426373.

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As the amount of accessible biological data is growing exponentially, it is becoming harder and harder to extract the biological knowledge contained in thousands of databases. Biomedical scientists collect facts, often recording them in natural language, and then use their knowledge to make inferences about as yet uncharacterised observations. Therefore, to make the best use of biological databases and the knowledge they contain, different kinds of information from different sources must be integrated in ways that make sense to the scientific community. The Gene Ontology (GO) and other biomed
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Kim, Wooyoung. "Innovative Algorithms and Evaluation Methods for Biological Motif Finding." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/63.

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Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles i
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Pohl, Matin. "Using an ontology to enhance metabolic or signaling pathway comparisions by biological and chemical knowledge." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-32.

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<p>Motivation:</p><p>As genome-scale efforts are ongoing to investigate metabolic networks of miscellaneous organisms the amount of pathway data is growing. Simultaneously an increasing amount of gene expression data from micro arrays becomes available for reverse engineering, delivering e.g. hypothetical regulatory pathway data. To avoid outgrowing of data and keep control of real new informations the need of analysis tools arises. One vital task is the comparison of pathways for detection of similar functionalities, overlaps, or in case of reverse engineering, detection of known data corrobo
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REHMAN, HAFEEZ UR. "Integration and Analysis of Heterogeneous Biological Data." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2537092.

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We live in the era of networks. The power of networks is the most fundamental driving force behind the machinery of life. Living bodies stay alive through complex inter-regulations of biochemical networks and information flows through these networks with such a great intensity and complexity that it exceeds anything that the human ingenuity has been able to spawn so far. Due to this overwhelming complexity we have begun to see a rapid rise in studies aimed at explaining the fundamental concepts and hidden properties of such complex systems. This thesis provides a strong foundation of using net
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He, Xin. "A semi-automated framework for the analytical use of gene-centric data with biological ontologies." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25505.

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Motivation Translational bioinformatics(TBI) has been defined as ‘the development and application of informatics methods that connect molecular entities to clinical entities’ [1], which has emerged as a systems theory approach to bridge the huge wealth of biomedical data into clinical actions using a combination of innovations and resources across the entire spectrum of biomedical informatics approaches [2]. The challenge for TBI is the availability of both comprehensive knowledge based on genes and the corresponding tools that allow their analysis and exploitation. Traditionally, biological r
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Seth, Pawan. "STUDY OF THE RELATIONSHIP BETWEEN Mus musculus PROTEIN SEQUENCES AND THEIR BIOLOGICAL FUNCTIONS." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1176736255.

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Bahurek, Tomáš. "Dotazovací jazyk pro databáze biologických dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234998.

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With rising amount of biological data, biological databases are becoming more important each day. Knowledge discovery (identification of connections that were unknown at the time of data entry) is an essential aspect of these databases. To gain knowledge from these databases one has to construct complicated SQL queries, which requires advanced knowledge of SQL language and used database schema. Biologists usually don't have this knowledge, which creates need for tool, that would offer more intuitive interface for querying biological databases. This work proposes ChQL, an intuitive query langua
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Books on the topic "Biological ontology"

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Russo, Nicola. La biologia filosofica di Hans Jonas. Guida, 2004.

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Cicchese, Gennaro. Scienze informatiche e biologiche: Epistemologia e ontologia. Città nuova, 2011.

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Stamos, David N. Species Problem: Biological Species, Ontology, and the Metaphysics of Biology. Lexington Books/Fortress Academic, 2003.

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Stamos, David N. The Species Problem, Biological Species, Ontology, and the Metaphysics of Biology. Lexington Books, 2004.

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The Species Problem, Biological Species, Ontology, and the Metaphysics of Biology. Lexington Books, 2003.

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DiFrisco, James. Biological Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779636.003.0004.

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This chapter investigates the identity and persistence conditions for processes as a task of biological process ontology. It argues that the problem of intrinsic variation in evolution, development, and metabolism motivates viewing biological individuals as processes rather than as substances. Different criteria of identity for processes are then evaluated, including causal and spatio-temporal relations. The chapter ultimately settles on the view that processes are individuated by causal cohesion and are identical if they share the same cohesive properties and spatio-temporal region. The persi
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Meincke, Anne Sophie. Persons as Biological Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779636.003.0018.

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Human persons exist longer than a single moment in time; they persist through time. However, so far it has not been possible to make this natural and widespread assumption metaphysically comprehensible. The philosophical debate on personal identity is rather stuck in a dilemma: reductionist theories explain personal identity away, while non-reductionist theories fail to give any informative account at all. This chapter argues that this dilemma emerges from an underlying commitment, shared by both sides in the debate, to an ontology that gives priority to static unchanging things. The claim def
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Arnellos, Argyris. From Organizations of Processes to Organisms and Other Biological Individuals. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779636.003.0010.

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The emphasis on the collaborative dimension of life overlooks the importance of biological individuals (conceived of as integrated, self-maintaining organizations) in the build-up of more complex collaborative networks in the course of evolution. This chapter proposes a process-based organizational ontology for biology, according to which the essential features of unicellular organismicality are captured by a self-maintaining organization of processes integrated by means of a special type of collaboration (realized through regulatory processes entailing an indispensable interdependence) betwee
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Bouchard, Frédéric. Symbiosis, Transient Biological Individuality, and Evolutionary Processes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779636.003.0009.

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Whereas individual organisms have acted as the paradigm case to make us think about biological individuality, multi-organism assemblages such as colonies and communities force us to reconsider how biological individuality can emerge. Symbiosis research has given philosophers of biology tools for rethinking the nature of biological individuality. This chapter discusses how the adaptations linked to symbiotic communities highlight a new research dilemma: should we think of a biological ontology focused on individuals and their traits (even if this means positing non-orthodox individuals with non
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Bock, Juergen. Ontology Alignment using Biologically-inspired Optimisation Algorithms. KIT Scientific Publishing, 2013.

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Book chapters on the topic "Biological ontology"

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Mosterin, Jesus. "Mereology, Set Theory, Biological Ontology." In Logic and Philosophy of Science in Uppsala. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-015-8311-4_33.

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Wang, Jiguang. "Ontology Analysis of Biological Networks." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_487.

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Cho, Young-Rae. "Filtering Protein-protein Interactions By Integration of Ontology Data." In Biological Knowledge Discovery Handbook. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch04.

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Visconti, Alessia, Francesca Cordero, Dino Ienco, and Ruggero G. Pensa. "Coclustering Under Gene Ontology Derived Constraints for Pathway Identification." In Biological Knowledge Discovery Handbook. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch27.

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Kim, Ju Han. "Gene Ontology and Biological Pathway-Based Analysis." In Genome Data Analysis. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_7.

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Keet, Catharina Maria. "Ontology-Driven Formal Conceptual Data Modeling for Biological Data Analysis." In Biological Knowledge Discovery Handbook. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch06.

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Perez-Rey, David, Alberto Anguita, and Jose Crespo. "OntoDataClean: Ontology-Based Integration and Preprocessing of Distributed Data." In Biological and Medical Data Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946465_24.

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Chhaya, Pradeep. "Biological Algorithm of Involution: Ontology of Gene Expressions." In The Topological Model of Genome and Evolution. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4318-0_4.

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Koutkias, Vassilis, Andigoni Malousi, and Nicos Maglaveras. "Performing Ontology-Driven Gene Prediction Queries in a Multi-agent Environment." In Biological and Medical Data Analysis. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_38.

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Dey, Lipika, and Muhammad Abulaish. "Enhancing a Biological Concept Ontology to Fuzzy Relational Ontology with Relations Mined from Text." In Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908029_55.

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Conference papers on the topic "Biological ontology"

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Moreno-Perdomo, Delia Aminta, Paloma Tejera-Nevado, Lucía Prieto-Santamaría, Guillermo Vigueras, Antonio Jesus Diaz-Honrubia, and Alejandro Rodríguez-González. "Lung-CABO: Lung Cancer Concepts Association Biological Ontology." In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2025. https://doi.org/10.1109/cbms65348.2025.00136.

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Ersoz, Nur, Burcu Bakir-Gungor, and Malik Yousef. "Identifying Inflammatory Bowel Disease-Associated Gene Ontology Groups Using Biological Knowledge-Based Machine Learning." In 16th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013178400003911.

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KUSHIDA, TATSUYA, TOSHIHISA TAKAGI, and KEN ICHIRO FUKUDA. "EVENT ONTOLOGY: A PATHWAY-CENTRIC ONTOLOGY FOR BIOLOGICAL PROCESSES." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701626_0015.

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McPherson, Jeffrey D., Ian R. Grosse, Sundar Krishnamurty, Jack C. Wileden, Elizabeth R. Dumont, and Michael A. Berthaume. "Integrating Biological and Engineering Ontologies." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13527.

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As methods for engineering data acquisition improve, methods for storing, generating knowledge from, and sharing that data for efficient reuse have become more important. Knowledge management in the engineering community can greatly benefit from advancements made in knowledge management in biology. The biological community has already made progress in knowledge management through projects such as the Gene Ontology and CellML, and it behooves the engineering community to learn from their successes. Engineering and biology overlap in the field of biosimulation, (i.e. finite-element analysis of b
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Huang, Minlie, Xiaoyan Zhu, Shilin Ding, Hao Yu, and Ming Li. "ONBIRES: Ontology-Based Biological Relation Extraction System." In 4th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2005. http://dx.doi.org/10.1142/9781860947292_0036.

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Keyuan Jiang and C. Nash. "Ontology-Based Aggregation of Biological Pathway Datasets." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616307.

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Devkota, Pratik, Somya Mohanty, and Prashanti Manda. "Ontology-Powered Boosting for Improved Recognition of Ontology Concepts from Biological Literature." In 14th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011683200003414.

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Xu, Qing-Wei, Li Yang, and Jiang Li. "Comprehensive Gene Ontology Mapping Strategies for Improved Biological Inference." In 2009 IITA International Conference on Control, Automation and Systems Engineering, CASE 2009. IEEE, 2009. http://dx.doi.org/10.1109/case.2009.103.

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Yang, Guoxia, Ziyu Liu, and Xiaomin Shen. "Research on Construction Method of Agricultural Domain Ontology." In 2015 International Conference on Materials, Environmental and Biological Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/mebe-15.2015.45.

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Jiang, Ying, Wenting Cheng, and Fangjie Wang. "Dimensional Ontology based fuzzy knowledge modeling of biological adjustment network." In 2012 International Conference on System Science and Engineering (ICSSE). IEEE, 2012. http://dx.doi.org/10.1109/icsse.2012.6257240.

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