In Silico Hypothesis Discovery - Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare - page 123

Chapter 8
In Silico Hypothesis Discovery

Philip R. O. Payne

By the End of This Chapter, Readers Should Be Able to :

Understand the role of conceptual knowledge collections in terms of informing the
design and use of reasoning systems for the purpose of in silico hypothesis discovery
￿ Select appropriate evaluation methodologies that can be used the assess the per-
formance of in silico hypothesis discovery tools and platforms

￿ Identify open research questions related to the future of high-throughput hypoth-

esis generation and the impact of such innovations on current and future scien-
tifi c and healthcare delivery paradigms.

8.1 Introduction

As noted in the preceding chapters, the fundamental methods needed to conduct
basic science, and clinical and translational research are very complex, involving a
multitude of actors, workfl ows and data types. For example, the translational research
paradigm focuses on cyclical fl ow of data, information and knowledge between
laboratory researchers, clinical investigators and clinical or public health practitio-
ners, and is predicated on systems-level approaches that involve diverse information
needs, sources and management requirements [ 1 ]. A variety of reports and schol-
arly works have enumerated challenges that may prevent the effective conduct of
translational research. As introduced in Chap. 1 , one such challenge is commonly
known as the “T1 block” and is concerned with issues that impact the ability to
move data, information and knowledge between basic science and clinical research
settings. Similarly, a second challenge, often known as the “T2 block”, focuses upon
impediments affecting the movement of data, information and knowledge between