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Journal articles on the topic 'Biological ontology'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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6

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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7

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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8

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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11

Arias Domínguez, Asier. "El embrollo causal del naturalismo biológico." Daimon, no. 92 (May 1, 2024): 131–43. http://dx.doi.org/10.6018/daimon.458871.

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Biological naturalism’s mental ontology decisively depends on the notion of causality. However, Searle does not embed this notion in his ontology in dialogue with the relevant scientific and philosophical literature, but rather guided by a certain sort of intuitive verisimilitude. Taking Bunge’s general ontology as a background, we discuss two important problems rooted in this shallow treatment of causality: the first one has to do with the fact that Searlean emergentism builds upon a categorical error between properties and processes; the second one with the fact that Searle’s only argument f

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Mamatha, Ch, Dr V. Anandam, Priyadarshini Chatterjee, and Hepshiba Vijaya Kumari. "Attribute Based Image Retrieval and Segmentation using On-tological Approaches." International Journal of Engineering & Technology 7, no. 4.6 (2018): 103. http://dx.doi.org/10.14419/ijet.v7i4.6.20440.

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Content based image retrieval is gaining more and more importance as it is an apt approach to retrieve an image. The image is retrieved based on certain texture. Ontology is a branch of Meta Physics that helps in analyzing an input image based on certain textures. Ontology helps to retrieve an image based on its properties. Ontology describes a domain. With that domain, we can proceed further to understand the relation between the features present in the domain. There are biological-ontologies to analyze biological outcomes. The field of information technology can be combined with biological o

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13

Arguello, Casteleiro Mercedes, Julie Klein, and Robert Stevens. "The Proteasix Ontology." Journal of Biomedical Semantics 7, no. 1 (2016): 33. https://doi.org/10.1186/s13326-016-0078-9.

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<strong>Background: </strong>The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion <i>in silico</i> the proteases involved in the generation of proteolytic cleavage fragments (peptides)<strong>Methods: </strong>The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known pr

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14

Williams, Reginald. "RACE, CLASS, AND ONTOLOGY." Think 9, no. 24 (2010): 85–89. http://dx.doi.org/10.1017/s1477175609990261.

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Many who write on race consider it an ‘illusion’. Others argue that race is real, even if socially constructed, because the notion of race, and the categorizing of people in terms of race, has greatly affected their lives. This paper criticizes a reason that is often given for thinking that race is an illusion: the fact that there is no biological basis of race. I defend two primary claims. First, while there is no biological basis for membership in a socio-economic class, by which I mean one's being relatively affluent or poor in a particular society, no one takes socio-economic class to be a

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15

Kushida, Tatsuya, Kouji Kozaki, Takahiro Kawamura, Yuka Tateisi, Yasunori Yamamoto, and Toshihisa Takagi. "Interconnection of Biological Knowledge Using NikkajiRDF and Interlinking Ontology for Biological Concepts." New Generation Computing 37, no. 4 (2019): 525–49. http://dx.doi.org/10.1007/s00354-019-00074-y.

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Abstract We investigated the interconnection on knowledge of biological molecules, biological phenomena, and diseases to efficiently collect information regarding the functions of chemical compounds and gene products, roles, applications, and involvements in diseases using knowledge graphs (KGs) developed from Resource Description Framework (RDF) data and ontologies. NikkajiRDF linked open data provide information on approximately 3.5 million chemical compounds and 694 application examples. We integrated NikkajiRDF with Interlinking Ontology for Biological Concepts (IOBC), including approximat

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16

Myhre, S., H. Tveit, T. Mollestad, and A. Laegreid. "Additional Gene Ontology structure for improved biological reasoning." Bioinformatics 22, no. 16 (2006): 2020–27. http://dx.doi.org/10.1093/bioinformatics/btl334.

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17

Thomas, Paul D., Huaiyu Mi, and Suzanna Lewis. "Ontology annotation: mapping genomic regions to biological function." Current Opinion in Chemical Biology 11, no. 1 (2007): 4–11. http://dx.doi.org/10.1016/j.cbpa.2006.11.039.

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18

Sanfilippo, Antonio, Bob Baddeley, Nathaniel Beagley, et al. "Using the gene ontology to enrich biological pathways." International Journal of Computational Biology and Drug Design 2, no. 3 (2009): 221. http://dx.doi.org/10.1504/ijcbdd.2009.030114.

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19

Buttigieg, Pier, 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. 1 (2013): 43. http://dx.doi.org/10.1186/2041-1480-4-43.

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20

Kohler, J., S. Philippi, and M. Lange. "SEMEDA: ontology based semantic integration of biological databases." Bioinformatics 19, no. 18 (2003): 2420–27. http://dx.doi.org/10.1093/bioinformatics/btg340.

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21

Jusufi, Ilir, Andreas Kerren, and Falk Schreiber. "Exploring biological data: Mappings between ontology- and cluster-based representations." Information Visualization 12, no. 3-4 (2013): 291–307. http://dx.doi.org/10.1177/1473871612468880.

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Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set but are usually considered independently. However, often

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22

Krušič, Martina, Gregor Jezernik, and Uroš Potočnik. "Gene Ontology Analysis Highlights Biological Processes Influencing Responsiveness to Biological Therapy in Psoriasis." Pharmaceutics 15, no. 8 (2023): 2024. http://dx.doi.org/10.3390/pharmaceutics15082024.

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Psoriasis is a chronic, immune-mediated and inflammatory skin disease. Although various biological drugs are available for psoriasis treatment, some patients have poor responses or do not respond to treatment. The aim of the present study was to highlight the molecular mechanism of responsiveness to current biological drugs for psoriasis treatment. To this end, we reviewed previously published articles that reported genes associated with treatment response to biological drugs in psoriasis, and gene ontology analysis was subsequently performed using the Cytoscape platform. Herein, we revealed a

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23

Jeong, Euna, Masao Nagasaki, Ayumu Saito, and Satoru Miyano. "Cell System Ontology: Representation for Modeling, Visualizing, and Simulating Biological Pathways." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 7, no. 6 (2007): 623–38. https://doi.org/10.3233/isi-2007-00335.

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With the rapidly accumulating knowledge of biological entities and networks, there is a growing need for a general framework to understand this information at a system level. In order to understand life as system, a formal description of system dynamics with semantic validation will be necessary. Within the context of biological pathways, several formats have been proposed, e.g., SBML, CellML, and BioPAX. Unfortunately, these formats lack the formal definitions of each term or fail to capture the system dynamics behavior. Thus, we have developed a new system dynamics centered ontology called C

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24

Wood, Valerie, Seth Carbon, Midori A. Harris, et al. "Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns." Open Biology 10, no. 9 (2020): 200149. http://dx.doi.org/10.1098/rsob.200149.

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Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotati

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25

Zhang, Yu, Haitao Wu, Jinfeng Gao, Yongtao Zhang, Ruxian Yao, and Yuxiang Zhu. "Effective method for detecting error causes from incoherent biological ontologies." Mathematical Biosciences and Engineering 19, no. 7 (2022): 7388–409. http://dx.doi.org/10.3934/mbe.2022349.

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<abstract><p>Computing the minimal axiom sets (MinAs) for an unsatisfiable class is an important task in incoherent ontology debugging. Ddebugging ontologies based on patterns (DOBP) is a pattern-based debugging method that uses a set of heuristic strategies based on four patterns. Each pattern is represented as a directed graph and the depth-first search strategy is used to find the axiom paths relevant to the MinAs of the unsatisfiable class. However, DOBP is inefficient when a debugging large incoherent ontology with a lot of unsatisfiable classes. To solve the problem, we first

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26

MILED, ZINA BEN, YUE W. WEBSTER, YANG LIU, and NIANHUA LI. "AN ONTOLOGY FOR SEMANTIC INTEGRATION OF LIFE SCIENCE WEB DATABASES." International Journal of Cooperative Information Systems 12, no. 02 (2003): 275–94. http://dx.doi.org/10.1142/s0218843003000747.

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The incompatibilities among complex data formats and various schema used by biological databases that house these data are becoming a bottleneck in biological research. For example, biological data format varies from simple words (e.g. gene name), numbers (e.g. molecular weight) to sequence strings (e.g. nucleic acid sequence), to even more complex data formats such as taxonomy trees. Some information is embedded in narrative text, such as expert comments and publications. Some other information is expressed as graphs or images (e.g. pathways networks). The confederation of heterogeneous web d

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27

Devisscher, Martijn, Tim De Meyer, Wim Van Criekinge, and Peter Dawyndt. "An ontology based query engine for querying biological sequences." EMBnet.journal 19, B (2013): 51. http://dx.doi.org/10.14806/ej.19.b.729.

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28

Karp, P. D. "An ontology for biological function based on molecular interactions." Bioinformatics 16, no. 3 (2000): 269–85. http://dx.doi.org/10.1093/bioinformatics/16.3.269.

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29

Gamalielsson, Jonas, and Bjorn Olsson. "GOSAP: Gene Ontology-Based Semantic Alignment of Biological Pathways." International Journal of Bioinformatics Research and Applications 4, no. 3 (2008): 274. http://dx.doi.org/10.1504/ijbra.2008.019575.

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30

Wang, Haiying, Huiru Zheng, and Francisco Azuaje. "Ontology- and graph-based similarity assessment in biological networks." Bioinformatics 26, no. 20 (2010): 2643–44. http://dx.doi.org/10.1093/bioinformatics/btq477.

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31

Dai, Xiao Peng, and Wang Hao. "A Method of Knowledge Integration of Agricultural for Biological Disaster Alarming." Advanced Materials Research 765-767 (September 2013): 448–51. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.448.

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The correct basis for early warning of biological disasters is to master the knowledge of biological disasters. In the paper, Analysis Knowledge integration of the idea of biological disasters. Proposed a framework for knowledge integration of biological disasters and Constructing ontology of biological disasters

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32

Gan, Mingxin, Xue Dou, and Rui Jiang. "From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/793091.

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Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologie

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33

Gençoğlu, Muharrem Tuncay. "Ontology of Stochastic Differential Equations." Turkish Journal of Science and Technology 20, no. 1 (2024): 41–53. https://doi.org/10.55525/tjst.1561165.

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This study provides a comprehensive examination of the mathematical formulations, ontological foundations, and application domains of stochastic differential equations (SDEs). SDEs play a critical role in modeling complex phenomena such as uncertainty and randomness and can be applied across a wide range of fields from financial markets to biological systems. The paper contrasts the mathematical approaches of Itô and Stratonovich calculus, detailing the solution methods and theoretical foundations of SDEs. Additionally, the ontological foundations of SDEs and their applications in various scie

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34

Almasoud, Ameera, Hend Al-Khalifa, AbdulMalik Al-salman, and Miltiadis Lytras. "A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing." Applied Sciences 10, no. 20 (2020): 7092. http://dx.doi.org/10.3390/app10207092.

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Massive heterogeneous big data residing at different sites with various types and formats need to be integrated into a single unified view before starting data mining processes. Furthermore, in most of applications and research, a single big data source is not enough to complete the analysis and achieve goals. Unfortunately, there is no general or standardized integration process; the nature of an integration process depends on the data type, domain, and integration purpose. Based on these parameters, we proposed, implemented, and tested a big data integration framework that integrates big dat

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35

Fichtmüller, David, Fabian Reimeier, and Anton Güntsch. "Using Wikibase as a Platform to Develop a Semantic TDWG Standard." Biodiversity Information Science and Standards 3 (June 19, 2019): e37212. https://doi.org/10.3897/biss.3.37212.

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In the ABCD 3.0 Project the ABCD (Access to Biological Collection Data) Standard (Access to Biological Collections Data task group 2007) was transformed from a classic XML Schema into an OWL (Web Ontology Language) ontology (along side an updated semantic-aware XML version). While it was initially planned to use the established TDWG Terms wiki as the editing and development platform for the ABCD ontology, the rise of Wikidata and its underlying platform Wikibase have caused us to reconsider this decision and switch to a Wikibase installation instead. This proved to be a crucial decision, as Wi

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36

Rzeźnicka-Krupa, Jolanta. "Social Ontologies of Disability - Towards the Discursive Interpretations of Embodied Difference." Studia z Teorii Wychowania XIII, no. 4 (41) (2022): 9–24. http://dx.doi.org/10.5604/01.3001.0016.1633.

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Within disability research philosophical issues are becoming increasingly important in line with the paradigmatic change connected with social model of disability. Socio-cultural approach to this phenomenon entails the necessary to reformulate the ontological status of the categories of the body, corporeality, materiality, the notion of biological impairment, as well as the notion of disability itself. The article presents a reconstruction of some theoretical approaches to disability, using epistemological tools derived from Laclau’s political theory of discourse. In the paper, I seek to answe

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37

Flanagan, Keith, Robert Stevens, Matthew Pocock, Pete Lee, and Anil Wipat. "Ontology for Genome Comparison and Genomic Rearrangements." Comparative and Functional Genomics 5, no. 6-7 (2004): 537–44. http://dx.doi.org/10.1002/cfg.436.

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We present an ontology for describing genomes, genome comparisons, their evolution and biological function. This ontology will support the development of novel genome comparison algorithms and aid the community in discussing genomic evolution. It provides a framework for communication about comparative genomics, and a basis upon which further automated analysis can be built. The nomenclature defined by the ontology will foster clearer communication between biologists, and also standardize terms used by data publishers in the results of analysis programs. The overriding aim of this ontology is

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38

Cao, Shun-Liang, Lei Qin, Wei-Zhong He, Yang Zhong, Yang-Yong Zhu, and Yi-Xue Li. "Semantic Search among Heterogeneous Biological Databases Based on Gene Ontology." Acta Biochimica et Biophysica Sinica 36, no. 5 (2004): 365–70. http://dx.doi.org/10.1093/abbs/36.5.365.

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Abstract Semantic search is a key issue in integration of heterogeneous biological databases. In this paper, we present a methodology for implementing semantic search in BioDW, an integrated biological data warehouse. Two tables are presented: the DB2GO table to correlate Gene Ontology (GO) annotated entries from BioDW data sources with GO, and the semantic similarity table to record similarity scores derived from any pair of GO terms. Based on the two tables, multifarious ways for semantic search are provided and the corresponding entries in heterogeneous biological databases in semantic term

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39

Avchenko, Oleg V. "Not Observed Ontology." Voprosy Filosofii, no. 1 (2020): 37–43. http://dx.doi.org/10.21146/0042-8744-2021-1-37-43.

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Two narratives – natural science and religious, intersect in the area of unobservable ontology – an immaterial, transcendental, but real area that paradoxically exists outside and inside ordinary physical space-time. It is assumed that mathematical constructs, physical laws, physical constants, quantum objects, and even biological laws can be associated with this area. It is argued that physical laws are not invented by man, but are discovered, since they contain physical constants measured in special experimental works. Universal constants were not invented for reasons of convenience – p

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Avchenko, Oleg V. "Not Observed Ontology." Voprosy Filosofii, no. 1 (2020): 37–43. http://dx.doi.org/10.21146/0042-8744-2021-1-37-43.

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Two narratives – natural science and religious, intersect in the area of unobservable ontology – an immaterial, transcendental, but real area that paradoxically exists outside and inside ordinary physical space-time. It is assumed that mathematical constructs, physical laws, physical constants, quantum objects, and even biological laws can be associated with this area. It is argued that physical laws are not invented by man, but are discovered, since they contain physical constants measured in special experimental works. Universal constants were not invented for reasons of convenience – p

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41

Lagreid, A. "Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns." Genome Research 13, no. 5 (2003): 965–79. http://dx.doi.org/10.1101/gr.1144503.

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42

Gaudet, Pascale, Jeffery G. Williams, Petra Fey, and Rex L. Chisholm. "An anatomy ontology to represent biological knowledge in Dictyostelium discoideum." BMC Genomics 9, no. 1 (2008): 130. http://dx.doi.org/10.1186/1471-2164-9-130.

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43

Gupta, Amarnath, Christopher Condit, and Xufei Qian. "BioDB: An ontology-enhanced information system for heterogeneous biological information." Data & Knowledge Engineering 69, no. 11 (2010): 1084–102. http://dx.doi.org/10.1016/j.datak.2010.07.003.

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Jezernik, Gregor, Damjan Glavač, Pavel Skok, Martina Krušič, Uroš Potočnik, and Mario Gorenjak. "Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers." International Journal of Molecular Sciences 25, no. 18 (2024): 10210. http://dx.doi.org/10.3390/ijms251810210.

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Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive networks where non-coding RNA (ncRNA) is thought to play an essential role. As current gene ontology is based predominantly on protein-level information, there is a growing need to analyze networks with ncRNA. In this study, we propose a gene ontology workflow integrating ncRNA using the NPInter V5.0 database. To validate the proposed workflow, we

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45

Bresell, Anders, Bo Servenius, and Bengt Persson. "Ontology Annotation Treebrowser." Applied Bioinformatics 5, no. 4 (2006): 225–36. http://dx.doi.org/10.2165/00822942-200605040-00005.

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46

Yargan, Dilek, and Ludger Jansen. "Terminological Resources for Biologically Inspired Design and Biomimetics: Evaluation of the Potential for Ontology Reuse." Biomimetics 10, no. 1 (2025): 39. https://doi.org/10.3390/biomimetics10010039.

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Biomimetics aims to learn from living systems to develop innovative technical artefacts. As it transcends disciplinary boundaries and needs to integrate both biological and technological knowledge, a domain ontology for biomimetics would be highly desirable. So far, several terminological resources have been designed to support the biomimetic development process. This paper examines nine resources for Biologically Inspired Design and biomimetics, including taxonomies, thesauri, and ontologies. Their benefits and limitations for structuring or organising biomimetic knowledge are evaluated again

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47

Pafilis, Evangelos, Rūdolfs Bērzinš, Christos Arvanitidis, and Lars Jensen. "EXTRACT 2.0: interactive identification of biological entities mentioned in text to assist database curation and knowledge extraction." Biodiversity Information Science and Standards 1 (August 10, 2017): e20152. https://doi.org/10.3897/tdwgproceedings.1.20152.

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Data curation is a process occurring in many aspects of biodiversity research, e.g. in digitization of specimen collections and extraction of species occurrences from the legacy literature. Data curation is always characterized by being time demanding and tedious. Gathering information on species and exposing it via search interfaces could be facilitated once phrases of interest have been recognized and the mentioned entities have been linked to community resources. A curator can benefit from interactive systems that highlight biological entities in a document, indicating sections of interest,

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48

Chen, Huajun, Xi Chen, Peiqin Gu, Zhaohui Wu, and Tong Yu. "OWL Reasoning Framework over Big Biological Knowledge Network." BioMed Research International 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/272915.

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Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive bi

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49

Golovan, Serguei, and Mainul Husain. "Identification of biologically significant genes using Gene Ontology (GO) and pathways analysis (144.16)." Journal of Immunology 184, no. 1_Supplement (2010): 144.16. http://dx.doi.org/10.4049/jimmunol.184.supp.144.16.

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Abstract Due to the small sample size and high dimensionality, it is not always possible to identify all of the biologically relevant genes from microarray analysis by simply selecting an arbitrary fold change and p-value thresholds. Such concerns are especially relevant in the analysis of immune response. It has been long known that large variability exist in immune responsiveness with the coefficient of variation (CV) as high as 230% reported for antibody response. Similarly, immune related genes were shown to have CV in excess of 100%. As a consequence, some genes involved in immune respons

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Rapoport, Rachel, Avraham Greenberg, Zohar Yakhini, and Itamar Simon. "A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms." Biology 13, no. 3 (2024): 175. http://dx.doi.org/10.3390/biology13030175.

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Abstract:

Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes’ spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomi

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