Studies on Semantic Systems Chemical Biology

Autor/in	Chen, Bin
Titel	Studies on Semantic Systems Chemical Biology
Quelle	(2012), (139 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Indiana University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-2676-1920-4
Schlagwörter	Hochschulschrift; Dissertation; Biology; Information Science; Web 2.0 Technologies; Data Collection; Pharmacology; Semantics; Models; Topology + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Biologie; Informationswissenschaft; Data capture; Datensammlung; Arzneikunde; Semantik; Analogiemodell; Topologie
Abstract	Current "one disease, one target and one drug" drug development paradigm is under question as relatively few drugs have reached the market in the last two decades. Increasingly research focus is being placed on the study of drug action against biological systems as a whole rather than against a single component (called "Systems Chemical Biology"). This has been made possible by the rapid accumulation of various data in chemistry and biology. Systematic integration of these heterogeneous sets and the provision of algorithms to mine the integrated sets would permit investigation of the complex mechanisms of action of drugs; however, traditional approaches face challenges in the representation and integration of multi-scale data sets and in the discovery of underlying knowledge from the integrated large-scale data sets. The Semantic Web, envisioned to enable machines to understand and respond to complex human requests and to retrieve relevant, yet distributed data, has potential to assist systems chemical biology studies. In the dissertation study, I explore cutting edge Semantic Web technologies to represent and integrate data and leverage the network models to mine the integrated data. I first create a single RDF repository called Chem2Bio2RDF by aggregating over 20 data resources, and use it to investigate polypharmacology and identify potential multiple pathway inhibitors. I further develop a generalized OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. I use the ontology to annotate the Chem2Bio2RDF dataset, creating a large heterogeneous network with semantic meanings. I further develop a novel statistical model to assess the entity associations based on the topology and semantics of the network, and apply it to assess drug-target associations that assist the investigation of the mechanisms of action of drugs. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10