title: The TOKEn project: knowledge synthesis for in silico science.
abstract: OBJECTIVE
  The conduct of investigational studies that involve large-scale data sets presents significant challenges related to the discovery and testing of novel hypotheses capable of supporting in silico discovery science. The use of what are known as Conceptual Knowledge Discovery in Databases (CKDD) methods provides a potential means of scaling hypothesis discovery and testing approaches for large data sets. Such methods enable the high-throughput generation and evaluation of knowledge-anchored relationships between complexes of variables found in targeted data sets.
  
  
  METHODS
  The authors have conducted a multipart model formulation and validation process, focusing on the development of a methodological and technical approach to using CKDD to support hypothesis discovery for in silico science. The model the authors have developed is known as the Translational Ontology-anchored Knowledge Discovery Engine (TOKEn). This model utilizes a specific CKDD approach known as Constructive Induction to identify and prioritize potential hypotheses related to the meaningful semantic relationships between variables found in large-scale and heterogeneous biomedical data sets.
  
  
  RESULTS
  The authors have verified and validated TOKEn in the context of a translational research data repository maintained by the NCI-funded Chronic Lymphocytic Leukemia Research Consortium. Such studies have shown that TOKEn is: (1) computationally tractable; and (2) able to generate valid and potentially useful hypotheses concerning relationships between phenotypic and biomolecular variables in that data collection.
  
  
  CONCLUSIONS
  The TOKEn model represents a potentially useful and systematic approach to knowledge synthesis for in silico discovery science in the context of large-scale and multidimensional research data sets.
authors: Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA. philip.payne@osumc.edu
authors: Philip R O
authors: PR
authors: Payne
authors: Tara B
authors: TB
authors: Borlawsky
authors: Omkar
authors: O
authors: Lele
authors: Stephen
authors: S
authors: James
authors: Andrew W
authors: AW
authors: Greaves
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019992:Databases as Topic; D016247:Information Storage and Retrieval; D020407:Internet; D008962:Models, Theoretical