In Silico Hypothesis Discovery - Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare - page 140

their consistency and performance. In addition, logical methods can be utilized to
examine axioms and assess the existence of unnecessary or redundant relationships
within the knowledge collection. One of the most common approaches to imple-
menting this type of evaluation is the representation of the individual hypothesis
generated by the agent as formal ontological constructs within the Protegé knowl-
edge editor [ 44 ]. Once such hypotheses have been represented in Protégé, logical
axioms can be extracted and evaluated using the Protegé Axiom Language (PAL)
extension [ 45 ]. An example of this method can be found in the formal evaluation of
the logical consistency of the Gene Ontology (GO) [ 46 ] reported by Yeh et al. [ 45 ].

8.4.6 Hybrid Methods

As described earlier, hybrid methods for verifying or validating knowledge col-
lections involve the use of techniques belonging to two or more of the classes of
measures as described above. An example of such a hybrid method is the novel
computational simulation approach to validating the results of multi-expert cat-
egorical sorting studies as proposed by Payne and Starren [ 47 ]. This approach
measures multi-source agreement using a combination of quantitative and graph
theoretic methods. Another example of a hybrid technique is the use of hypothesis
discovery methods, such as hierarchical clustering [ 48 ] to determine the degree of
interrelatedness of a knowledge collection. Such evaluative methods combine sta-
tistical, heuristic and graph theoretic techniques.

8.5 Implications for Stakeholders

It can be seen that each of the different stakeholders described in Chap. 1 benefi ts
realizing the vision of a Translational Informatics model that enables and facilitates
knowledge-driven healthcare. With specifi c regard to the concepts associated with
in silico hypothesis discovery, these benefi ts are multi-fold, and largely focus upon
the accelerated pace and ease with which new diagnostic and therapeutic discover-
ies can be generated from existing or new data sets. Specifi c benefi ts at all of the
levels introduced in Chap. 1 include:

8.5.1 Evidence and Policy Generators

￿ Investments in the creation of large-scale and multi-dimensional data sets

can exhibit much higher returns on investment owing to the ability to gener-
ate a larger number of testable and potentially clinically actionable hypotheses
from those resources;