By Mihail Popescu, Dong Xu
An ontology is a suite of vocabulary phrases with explicitly acknowledged meanings and kin with different phrases. almost immediately, more and more ontologies are being outfitted and used for annotating facts in biomedical examine. due to the super volume of knowledge being generated, ontologies at the moment are getting used in several methods, together with connecting diversified databases, refining seek services, examining experimental/clinical info, and inferring wisdom. This state-of-the-art source introduces researchers to most recent advancements in bio-ontologies. The e-book offers the theoretical foundations and examples of ontologies, in addition to purposes of ontologies in biomedicine, from molecular degrees to medical degrees. Readers additionally locate info on technological infrastructure for bio-ontologies. This complete, one-stop quantity offers quite a lot of useful bio-ontology details, delivering pros special assistance within the clustering of organic facts, protein type, gene and pathway prediction, and textual content mining.
Read Online or Download Data Mining in Biomedicine Using Ontologies (Artech House Series Bioinformatics & Biomedical Imaging) PDF
Similar data mining books
Do you converse facts and knowledge to stakeholders? This factor is a component 1 of a two-part sequence on facts visualization and overview. partly 1, we introduce contemporary advancements within the quantitative and qualitative facts visualization box and supply a ancient standpoint on facts visualization, its power position in evaluate perform, and destiny instructions.
Colossal info Imperatives, specializes in resolving the main questions about everyone’s brain: Which info concerns? Do you might have adequate information quantity to justify the utilization? the way you are looking to technique this volume of knowledge? How lengthy do you really want to maintain it lively in your research, advertising, and BI functions?
This publication introduces significant Purposive interplay research (MPIA) idea, which mixes social community research (SNA) with latent semantic research (LSA) to aid create and examine a significant studying panorama from the electronic strains left by means of a studying group within the co-construction of information.
This publication constitutes the refereed court cases of the tenth Metadata and Semantics study convention, MTSR 2016, held in Göttingen, Germany, in November 2016. The 26 complete papers and six brief papers offered have been conscientiously reviewed and chosen from sixty seven submissions. The papers are prepared in different classes and tracks: electronic Libraries, info Retrieval, associated and Social information, Metadata and Semantics for Open Repositories, learn info structures and knowledge Infrastructures, Metadata and Semantics for Agriculture, foodstuff and surroundings, Metadata and Semantics for Cultural Collections and purposes, eu and nationwide tasks.
- Scala: Guide for Data Science Professionals
- Applied Data Mining : Statistical Methods for Business and Industry (Statistics in Practice)
- Fuzzy Databases: Modeling, Design And Implementation
- Multiple Classifier Systems: 12th International Workshop, MCS 2015, Günzburg, Germany, June 29 - July 1, 2015, Proceedings
- Scalable Uncertainty Management: 8th International Conference, SUM 2014, Oxford, UK, September 15-17, 2014. Proceedings
Extra resources for Data Mining in Biomedicine Using Ontologies (Artech House Series Bioinformatics & Biomedical Imaging)
This list is not exhaustive, but the most prominent examples are highlighted. 1 Upper Ontologies Upper ontologies are often referred to as top ontologies or foundational ontologies. They strongly reflect the philosophical roots of ontological classification. They do not cover any specific domain or application, and instead make very broad distinctions about existence. An upper ontology would allow a distinction like continuant (things that exist, such as objects) versus occurrent (things that happen, such as processes), and hence, provide a way of being more specific about the fundamental differences between the two classes.
BioMeKe: An Ontology-Based Biomedical Knowledge Extraction System Devoted to Transcriptome Analysis,” Stud Health Technol Inform, Vol. 95, 2003, pp. 80–85. , “Beyond Concepts: Ontology as Reality Representation,” Formal Ontology and Information Systems 2004, Toring, Italy, November 4–6, 2004, pp. 73–84. , General Formal Ontology (GFO): A Foundational Ontology Integrating Objects and Processes. Part I: Basic Principles, 2008, Research Group Ontologies in Medicine (Onto-Med), University of Leipzig.
Of FOIS ‘98, Trento, Italy, June 6–8, 1998, pp. 3–15. , “Modeling for Decision Support,” Handbook of Medical Informatics, J. v. Bemmel and M. A. ), 1997.  Gruber, T. , “The Role of Common Ontology in Achieving Sharable, Reusable Knowledge Bases,” Proceedings of KR 1991: Principles of Knowledge Representation and Reasoning, J. F. Allen, R. Fikes, and E. ), 1991, San Mateo, California: Morgan Kaufmann, pp. 601–602.  Gruber, T. , Toward Principles for the Design of Ontologies Used for Knowledge Sharing, Palo Alto, CA: Knowledge Systems Laboratory, Stanford University, 1993.