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SCIENTIA SINICA Vitae, Volume 43 , Issue 3 : 223-239(2013) https://doi.org/10.1360/052012-292

Construction, Assessment and Applications of Biomedical Ontologies

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  • AcceptedDec 24, 2012
  • PublishedMar 19, 2013

Abstract


References

[1] Bard J B, Rhee S Y. Ontologies in biology: design, applications and future challenges. Nat Rev Genet, 2004, 5: 213-222. Google Scholar

[2] 田云, 卢向阳. 生物信息学. 生物学杂志, 2002, 19: 11-12. Google Scholar

[3] 李健康, 张春辉. 本体研究及其应用进展. 图书馆论坛, 2004, 24: 80-86. Google Scholar

[4] Hill D P, Berardini T Z, Howe D G, et al. Representing ontogeny through ontology: a developmental biologist's guide to the gene ontology. Mol Reprod Dev, 2009, 77: 314-329. Google Scholar

[5] Rhee S Y, Wood V, Dolinski K, et al. Use and misuse of the gene ontology annotations. Nat Rev Genet, 2008, 9: 509-515. Google Scholar

[6] Schriml L M, Arze C, Nadendla S, et al. Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res, 2011, 40: D940-946. Google Scholar

[7] Segerdell E, Bowes J B, Pollet N, et al. An ontology for Xenopus anatomy and development. BMC Dev Biol, 2008, 8: 92. Google Scholar

[8] Rothwell D, Fritz A. SNOMED microcomputer software system. Pathologist, 1983, 37: 15-18. Google Scholar

[9] Lindberg D A, Humphreys B L, McCray A T. The Unified Medical Language System. Methods Inf Med, 1993, 32: 281-291. Google Scholar

[10] 夏燕, 张忠平, 曹顺良, 等. Gene Ontology在生物数据整合中的应用. 计算机工程, 2005, 31: 57-58. Google Scholar

[11] Stein L D. Integrating biological databases. Nat Rev Genet, 2003, 4: 337-345. Google Scholar

[12] 李勇, 张志刚. 领域本体构建方法研究. 计算机工程与科学, 2008, 30: 129-131. Google Scholar

[13] Yu A C. Methods in biomedical ontology. J Biomed Inform, 2006, 39: 252-266. Google Scholar

[14] Smith B, Ceusters W, Klagges B, et al. Relations in biomedical ontologies. Genome Biol, 2005, 6: R46. Google Scholar

[15] Lovering R C, Dimmer E C, Talmud P J. Improvements to cardiovascular gene ontology. Atherosclerosis, 2009, 205: 9-14. Google Scholar

[16] Torto-Alalibo T, Collmer C W, Gwinn-Giglio M, et al. Unifying themes in microbial associations with animal and plant hosts described using the gene ontology. Microbiol Mol Biol Rev, 2010, 74: 479-503. Google Scholar

[17] McCarthy F M, Mahony T J, Parcells M S, et al. Understanding animal viruses using the Gene Ontology. Trends Microbiol, 2009, 17: 328-335. Google Scholar

[18] Giglio M G, Collmer C W, Lomax J, et al. Applying the Gene Ontology in microbial annotation. Trends Microbiol, 2009, 17: 262-268. Google Scholar

[19] 宋丹辉. 本体评价研究综述. 情报理论与实践, 2011, 34: 118-122. Google Scholar

[20] Ivchenko O, Younesi E, Shahid M, et al. PLIO: an ontology for formal description of protein-ligand interactions. Bioinformatics, 2011, 27: 1684-1690. Google Scholar

[21] 孙坦, 宋丹辉. 本体模型中的错误类型及检测方法. 图书情报工作, 2011, 55: 84-89. Google Scholar

[22] Ashburner M, Ball C A, Blake J A, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000, 25: 25-29. Google Scholar

[23] Bresell A, Servenius B, Persson B. Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists. Appl Bioinformatics, 2006, 5: 225-236. Google Scholar

[24] Dwight S S, Harris M A, Dolinski K, et al. Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO). Nucleic Acids Res, 2002, 30: 69-72. Google Scholar

[25] Aslett M, Wood V. Gene Ontology annotation status of the fission yeast genome: preliminary coverage approaches 100%. Yeast, 2006, 23: 913-919. Google Scholar

[26] Arnaud M B, Costanzo M C, Shah P, et al. Gene Ontology and the annotation of pathogen genomes: the case of Candida albicans. Trends Microbiol, 2009, 17: 295-303. Google Scholar

[27] Torto-Alalibo T, Meng S, Dean R A. Infection strategies of filamentous microbes described with the Gene Ontology. Trends Microbiol, 2009, 17: 320-327. Google Scholar

[28] Hu J C, Karp P D, Keseler I M, et al. What we can learn about Escherichia coli through application of Gene Ontology. Trends Microbiol, 2009, 17: 269-278. Google Scholar

[29] Meng S, Brown D E, Ebbole D J, et al. Gene Ontology annotation of the rice blast fungus, Magnaporthe oryzae. BMC Microbiol, 2009, 9: S8. Google Scholar

[30] Camon E, Magrane M, Barrell D, et al. The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res, 2003, 13: 662-672. Google Scholar

[31] Xie H, Wasserman A, Levine Z, et al. Large-scale protein annotation through gene ontology. Genome Res, 2002, 12: 785-794. Google Scholar

[32] Arciero C, Somiari S B, Shriver C D, et al. Functional relationship and gene ontology classification of breast cancer biomarkers. Int J Biol Markers, 2003, 18: 241-272. Google Scholar

[33] Lu X, Zhai C, Gopalakrishnan V, et al. Automatic annotation of protein motif function with Gene Ontology terms. BMC Bioinformatics, 2004, 5: 122. Google Scholar

[34] Hennig S, Groth D, Lehrach H. Automated Gene Ontology annotation for anonymous sequence data. Nucleic Acids Res, 2003, 31: 3712-3715. Google Scholar

[35] Groth D, Lehrach H, Hennig S. GOblet: a platform for Gene Ontology annotation of anonymous sequence data. Nucleic Acids Res, 2004, 32: W313-317. Google Scholar

[36] Zhong S, Li C, Wong W H. ChipInfo: Software for extracting gene annotation and gene ontology information for microarray analysis. Nucleic Acids Res, 2003, 31: 3483-3486. Google Scholar

[37] Wolting C, McGlade C J, Tritchler D. Cluster analysis of protein array results via similarity of Gene Ontology annotation. BMC Bioinformatics, 2006, 7: 338. Google Scholar

[38] Muro E M, Perez-Iratxeta C, Andrade-Navarro M A. Amplification of the Gene Ontology annotation of Affymetrix probe sets. BMC Bioinformatics, 2006, 7: 159. Google Scholar

[39] Ochs M F, Peterson A J, Kossenkov A, et al. Incorporation of gene ontology annotations to enhance microarray data analysis. Methods Mol Biol, 2007, 377: 243-254. Google Scholar

[40] Daraselia N, Yuryev A, Egorov S, et al. Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks. BMC Bioinformatics, 2007, 8: 243. Google Scholar

[41] Nguyen C D, Gardiner K J, Cios K J. Protein annotation from protein interaction networks and Gene Ontology. J Biomed Inform, 2011, 44: 824-829. Google Scholar

[42] Chibucos M C, Collmer C W, Torto-Alalibo T, et al. Programmed cell death in host-symbiont associations, viewed through the Gene Ontology. BMC Microbiol, 2009, 9: S5. Google Scholar

[43] Torto-Alalibo T, Collmer C W, Lindeberg M, et al. Common and contrasting themes in host cell-targeted effectors from bacterial, fungal, oomycete and nematode plant symbionts described using the Gene Ontology. BMC Microbiol, 2009, 9: S3. Google Scholar

[44] Lovering R C, Camon E B, Blake J A, et al. Access to immunology through the Gene Ontology. Immunology, 2008, 125: 154-160. Google Scholar

[45] Wiwanitkit V. Interaction between leptin and leptin receptor in gastric carcinoma: gene ontology analysis. Rev Esp Enferm Dig, 2007, 99: 201-205. Google Scholar

[46] Harhay G P, Keele J W. Positional candidate gene selection from livestock EST databases using Gene Ontology. Bioinformatics, 2003, 19: 249-255. Google Scholar

[47] del Pozo A, Pazos F, Valencia A. Defining functional distances over gene ontology. BMC Bioinformatics, 2008, 9: 50. Google Scholar

[48] Wang H, Azuaje F, Bodenreider O, et al. Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships. In: Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2004. 25-31. Google Scholar

[49] Min H, Manion F J, Goralczyk E, et al. Integration of prostate cancer clinical data using an ontology. J Biomed Inform, 2009, 42: 1035-1045. Google Scholar

[50] Odicino F, Pecorelli S, Zigliani L, et al. History of the FIGO cancer staging system. Int J Gynaecol Obstet, 2008, 101: 205-210. Google Scholar

[51] Fleming I D. AJCC/TNM cancer staging, present and future. J Surg Oncol, 2001, 77: 233-236. Google Scholar

[52] 常青. 文本挖掘: 挖掘知识. 中国计算机用户, 2004, 24: 49-50. Google Scholar

[53] 黄凯峰, 何洁月. 基于生物医学文献的知识发现研究. 计算机技术与发展, 2008, 2: 62-65. Google Scholar

[54] Ruch P G A, Gobeill J, Lisacek F, et al. Using discourse analysis to improve text categorization in MEDLINE. Stud Health Technol Inform, 2007, 129: 710-715. Google Scholar

[55] Wee L J, Er E P, Ng L F, et al. In silico prediction of the granzyme B degradome. BMC Genomics, 2012, 12: S11. Google Scholar

[56] Lu Z, Kao H Y, Wei C H, et al. The gene normalization task in BioCreative III. BMC Bioinformatics, 2011, 12: S2. Google Scholar

[57] Arighi C N, Roberts P M, Agarwal S, et al. BioCreative III interactive task: an overview. BMC Bioinformatics, 2011, 12: S4. Google Scholar

[58] Arighi C N, Lu Z, Krallinger M, et al. Overview of the BioCreative III Workshop. BMC Bioinformatics, 2011, 12: S1. Google Scholar

[59] Campos D, Matos S, Lewin I, et al. Harmonisation of gene/protein annotations: towards a gold standard MEDLINE. Bioinformatics, 2012, 28: 1253-1261. Google Scholar

[60] Krallinger M, Leitner F, Vazquez M, et al. How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience. Database (Oxford), 2012, bas017. Google Scholar

[61] Chatr-Aryamontri A, Winter A, Perfetto L, et al. Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases. BMC Bioinformatics, 2011, 12: S8. Google Scholar

[62] Campbell S J, Gaulton A, Marshall J, et al. Visualizing the drug target landscape. Drug Discov Today, 2011, 17: S3-15. Google Scholar

[63] Wu Y, Liu M, Zheng W J, et al. Ranking gene-drug relationships in biomedical literature using latent dirichlet allocation. Pac Symp Biocomput, 2012, 422-433. Google Scholar

[64] Faro A, Giordano D, Spampinato C. Combining literature text mining with microarray data: advances for system biology modeling. Brief Bioinform, 2011, 13: 61-82. Google Scholar

[65] Yu W, Clyne M, Dolan S M, et al. GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique. BMC Bioinformatics, 2008, 9: 205. Google Scholar

[66] Guo Y, Korhonen A, Silins I, et al. Weakly supervised learning of information structure of scientific abstracts—is it accurate enough to benefit real-world tasks in biomedicine? Bioinformatics, 2011, 27: 3179-3185. Google Scholar

[67] Settles B. ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics, 2005, 21: 3191-3192. Google Scholar

[68] Hakenberg J, Gerner M, Haeussler M, et al. The GNAT library for local and remote gene mention normalization. Bioinformatics, 2011, 27: 2769-2771. Google Scholar

[69] Yeganova L, Smith L, Wilbur W J. Identification of related gene/protein names based on an HMM of name variations. Comput Biol Chem, 2004, 28: 97-107. Google Scholar

[70] Ohta T, Pyysalo S, Kim J D, et al. A re-evaluation of biomedical named entity-term relations. J Bioinform Comput Biol, 2010, 8: 917-928. Google Scholar

[71] Cano C, Monaghan T, Blanco A, et al. Collaborative text-annotation resource for disease-centered relation extraction from biomedical text. J Biomed Inform, 2009, 42: 967-977. Google Scholar

[72] Rosse C, Mejino J L Jr. A reference ontology for biomedical informatics: the Foundational Model of Anatomy. J Biomed Inform, 2003, 36: 478-500. Google Scholar

[73] Fragoso G, de Coronado S, Haber M, et al. Overview and utilization of the NCI thesaurus. Comp Funct Genomics, 2004, 5: 648-654. Google Scholar

[74] Nakai T, Bagarinao E, Tanaka Y, et al. Ontology for FMRI as a biomedical informatics method. Magn Reson Med Sci, 2008, 7: 141-155. Google Scholar

[75] Alterovitz G, Xiang M, Mohan M, et al. GO PaD: the Gene Ontology Partition Database. Nucleic Acids Res, 2007, 35: D322-327. Google Scholar

[76] Natale D A, Arighi C N, Barker W C, et al. Framework for a protein ontology. BMC Bioinformatics, 2007, 8: S1. Google Scholar

[77] Natale D A, Arighi C N, Barker W C, et al. The Protein Ontology: a structured representation of protein forms and complexes. Nucleic Acids Res, 2010, 39: D539-545. Google Scholar

[78] Bult C J, Drabkin H J, Evsikov A, et al. The representation of protein complexes in the Protein Ontology (PRO). BMC Bioinformatics, 2011, 12: 371. Google Scholar

[79] Eilbeck K, Lewis S E, Mungall C J, et al. The Sequence Ontology: a tool for the unification of genome annotations. Genome Biol, 2005, 6: R44. Google Scholar

[80] Mungall C J, Batchelor C, Eilbeck K. Evolution of the Sequence Ontology terms and relationships. J Biomed Inform, 2010, 44: 87-93. Google Scholar

[81] Moore B, Fan G, Eilbeck K. SOBA: sequence ontology bioinformatics analysis. Nucleic Acids Res, 2010, 38: W161-164. Google Scholar

[82] Laulederkind S J, Tutaj M, Shimoyama M, et al. Ontology searching and browsing at the Rat Genome Database. Database (Oxford), 2012, bas016. Google Scholar

[83] Du P, Feng G, Flatow J, et al. From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics, 2009, 25: i63-68. Google Scholar

[84] Osborne J D, Flatow J, Holko M, et al. Annotating the human genome with Disease Ontology. BMC Genomics, 2009, 10: S6. Google Scholar

[85] Li J, Gong B, Chen X, et al. DOSim: an R package for similarity between diseases based on Disease Ontology. BMC Bioinformatics, 2011, 12: 266. Google Scholar

[86] LePendu P, Musen M A, Shah N H. Enabling enrichment analysis with the Human Disease Ontology. J Biomed Inform, 2011, 44: S31-38. Google Scholar

[87] Robinson P N, Mundlos S. The human phenotype ontology. Clin Genet, 2010, 77: 525-534. Google Scholar

[88] Robinson P N, Kohler S, Bauer S, et al. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet, 2008, 83: 610-615. Google Scholar

[89] Kohler S, Schulz M H, Krawitz P, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet, 2009, 85: 457-464. Google Scholar

[90] Smith C L, Goldsmith C A, Eppig J T. The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol, 2005, 6: R7. Google Scholar

[91] Smith C L, Eppig J T. The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip Rev Syst Biol Med, 2009, 1: 390-399. Google Scholar

[92] Bult C J, Kadin J A, Richardson J E, et al. The Mouse Genome Database: enhancements and updates. Nucleic Acids Res, 2009, 38: D586-592. Google Scholar

[93] Zhang Y, De S, Garner J R, et al. Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information. BMC Med Genomics, 2010, 3: 1. Google Scholar

[94] Consortium P O. The Plant Ontology Consortium and plant ontologies. Comp Funct Genomics, 2002, 3: 137-142. Google Scholar

[95] Jaiswal P, Avraham S, Ilic K, et al. Plant Ontology (PO): a controlled vocabulary of plant structures and growth stages. Comp Funct Genomics, 2005, 6: 388-397. Google Scholar

[96] Avraham S, Tung C W, Ilic K, et al. The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations. Nucleic Acids Res, 2008, 36: D449-454. Google Scholar

[97] Bard J, Rhee S Y, Ashburner M. An ontology for cell types. Genome Biol, 2005, 6: R21. Google Scholar

[98] Meehan T F, Masci A M, Abdulla A, et al. Logical development of the cell ontology. BMC Bioinformatics, 2011, 12: 6. Google Scholar

[99] Masci A M, Arighi C N, Diehl A D, et al. An improved ontological representation of dendritic cells as a paradigm for all cell types. BMC Bioinformatics, 2009, 10: 70. Google Scholar

[100] Diehl A D, Augustine A D, Blake J A, et al. Hematopoietic cell types: prototype for a revised cell ontology. J Biomed Inform, 2010, 44: 75-79. Google Scholar

[101] de Coronado S, Haber M W, Sioutos N, et al. NCI Thesaurus: using science-based terminology to integrate cancer research results. Stud Health Technol Inform, 2004, 107: 33-37. Google Scholar

[102] Shah N H, Rubin D L, Espinosa I, et al. Annotation and query of tissue microarray data using the NCI Thesaurus. BMC Bioinformatics, 2007, 8: 296. Google Scholar

[103] Marquet G, Dameron O, Saikali S, et al. Grading glioma tumors using OWL-DL and NCI Thesaurus. AMIA Annu Symp Proc, 2007, 508-512. Google Scholar

[104] Brochhausen M, Weiler G, Cocos C, et al. The ACGT Master Ontology on cancer—a new terminology source for oncological practice. IEEE Comput Soc, 2008, 324-329. Google Scholar

[105] Brochhausen M, Spear A D, Cocos C, et al. The ACGT Master Ontology and its applications—towards an ontology-driven cancer research and management system. J Biomed Inform, 2010, 44: 8-25. Google Scholar

[106] Stoeckert C J Jr., Parkinson H. The MGED ontology: a framework for describing functional genomics experiments. Comp Funct Genomics, 2003, 4: 127-132. Google Scholar

[107] Whetzel P L, Parkinson H, Causton H C, et al. The MGED Ontology: a resource for semantics-based description of microarray experiments. Bioinformatics, 2006, 22: 866-873. Google Scholar

[108] Whetzel P L, Parkinson H, Stoeckert C J Jr. Using ontologies to annotate microarray experiments. Methods Enzymol, 2006, 411: 325-339. Google Scholar

[109] Zheng J, Stoyanovich J, Manduchi E, et al. AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments. Database (Oxford), 2011, bar045. Google Scholar

[110] Brinkley J F, Rosse C. The Digital Anatomist distributed framework and its applications to knowledge-based medical imaging. J Am Med Inform Assoc, 1997, 4: 165-183. Google Scholar

[111] Dameron O, Rubin D L, Musen M A. Challenges in converting frame-based ontology into OWL: the Foundational Model of Anatomy case-study. AMIA Annu Symp Proc, 2005, 181-185. Google Scholar

[112] Hunter A, Kaufman M H, McKay A, et al. An ontology of human developmental anatomy. J Anat, 2003, 203: 347-355. Google Scholar

[113] Noe A, Lockett E, Sweet D A. Use and evaluation of the World Wide Web as a tool to explore the human developmental anatomy center. Comput Med Imaging Graph, 1999, 23: 41-44. Google Scholar

[114] Hayamizu T F, Mangan M, Corradi J P, et al. The Adult Mouse Anatomical Dictionary: a tool for annotating and integrating data. Genome Biol, 2005, 6: R29. Google Scholar

[115] Sundberg J P, Sundberg B A, Schofield P. Integrating mouse anatomy and pathology ontologies into a phenotyping database: tools for data capture and training. Mamm Genome, 2008, 19: 413-419. Google Scholar

[116] Bowes J B, Snyder K A, Segerdell E, et al. Xenbase: a Xenopus biology and genomics resource. Nucleic Acids Res, 2008, 36: D761-767. Google Scholar

[117] Belmamoune M, Verbeek F J. Data integration for spatio-temporal patterns of gene expression of zebrafish development: the GEMS database. J Integr Bioinform, 2008, 5: 92. Google Scholar

[118] Degtyarenko K, de Matos P, Ennis M, et al. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res, 2008, 36: D344-350. Google Scholar

[119] de Matos P, Alcantara R, Dekker A, et al. Chemical Entities of Biological Interest: an update. Nucleic Acids Res, 2009, 38: D249-254. Google Scholar

[120] Degtyarenko K, Hastings J, de Matos P, et al. ChEBI: an open bioinformatics and cheminformatics resource. Curr Protoc Bioinformatics, 2009,. CrossRef Google Scholar

[121] Wohlgemuth G, Haldiya P K, Willighagen E, et al. The Chemical Translation Service--a web-based tool to improve standardization of metabolomic reports. Bioinformatics, 2010, 26: 2647-2648. Google Scholar

[122] d'Aquin M, Noy N F. Where to publish and find ontologies? A survey of ontology libraries. Web Semant, 2012, 11: 96-111. Google Scholar

[123] Noy N F, Shah N H, Whetzel P L, et al. BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res, 2009, 37: W170-173. Google Scholar

[124] Smith B, Ashburner M, Rosse C, et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007, 25: 1251-1255. Google Scholar

[125] Cote R G, Jones P, Martens L, et al. The Ontology Lookup Service: more data and better tools for controlled vocabulary queries. Nucleic Acids Res, 2008, 36: W372-376. Google Scholar

[126] Cote R, Reisinger F, Martens L, et al. The Ontology Lookup Service: bigger and better. Nucleic Acids Res, 2010, 38: W155-160. Google Scholar

[127] Aitken S, Korf R, Webber B, et al. COBrA: a bio-ontology editor. Bioinformatics, 2005, 21: 825-826. Google Scholar

[128] Liu K, Hogan W R, Crowley R S. Natural Language Processing methods and systems for biomedical ontology learning. J Biomed Inform, 2011, 44: 163-179. Google Scholar

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