Biomedical Informatics in Translational Research - Biomedical Informatics in Translational Research - page 4

nication of clinically significant problems into the laboratory for research and reso-
lution. The only true measure of success of this targeted research is in terms of what
translates into the clinic. Although this may seem logical, examples of such true suc-
cesses are somewhat limited because the driver of much of academic research still
focuses on research that may be significant for enhancing our understanding of biol-
ogy, but does not necessarily transcend into addressing more direct clinical needs
[1-5].
Chapter 2 addresses the clinical perspective that is necessary to support this view
of translational research, and Chapter 3 discusses the critical aspects of sample and
data collection and the quality control issues needed to support clinically based
research.
1.1.2 Systems Biology
Systems biology is most commonly interpreted as the aggregation and integration of
multiple approaches to analyze and define a system, that is, the “omics” perspective,
and then analysis of the behavior of the system based on these perspectives. This bot-
tom-up approach can only bring together those views that are available through the
application of existing (and evolving) technologies. It is easy to see that this
approach can be limited by the expectation that these technologies will provide a
complete picture of the entity being studied rather than multiple views, each with its
own contextual limitation. Of course, these technologies are neither comprehensive
enough to provide a complete picture of a patient, nor can they necessarily produce
data of equivalent quality or content. A more suitable approach to systems biology
may involve a top-down approach, first examining the behavior of the intact system
(e.g., a patient with or without disease symptoms), to more fully identify the critical
question and then determine the technology or technologies most appropriate to
addressing these questions. It is clear, however, that all results must be integrated
into a comprehensive data model [6, 7]. In the case of biomedical informatics, this
model should be patient-centric to enable the exploration of relationships among
the multiple views presented by the different technologies. It should be clear that this
top-down approach aligns directly with the translational medicine definition given
earlier.
Chapter 4 provides a general overview of the range of data in the “omics” fields,
as well as issues related to experimental design and implementation. Chapter 5
focuses on issues specifically related to genomic studies, and Chapter 6 presents the
proteomic perspective.
1.1.3 Personalized Medicine
Personalized medicine has focused on optimizing treatment to maximize efficacy
and minimize risk (i.e., therapeutic medicine), using the genetic makeup of the
patient. However, per the Wikipedia website, “Medicine is … concerned with main-
taining or restoring human health through the study, diagnosis, treatment and possi-
ble prevention of disease and injury.” So, ideally, personalized medicine should
incorporate and promote a significant component of preventive medicine, thus
aligning more closely with the non-U.S. clinical perspective in which prevention is a