Biomedical Research Innovation Language - Part I - Why and how

The assumptions behind the Biomedical Research Innovation Language (BRIL) is simple, not to say intuitive:

1. Research innovation, defined as the ability to create scientific knowledge that will affect peers or society at large, is frequently created in the edge between or among scientific disciplines (Moore, 2003, Hukkinen, 2006). This intersection usually creates new knowledge that goes beyond the mere sum of information of the component disciplines.

2. Research innovation does not arise from simply putting together any two or more disciplines together, but arises only when these disciplines have areas that can make use of resources from each other to further the scientific knowledge in a new, joint discipline. In other words, the scientific disciplines have to be complimentary, a term that will be further defined in later posts.

3. Scientific disciplines can be represented in many forms, but for operational purposes a research group can be considered as a functional unit that partially carries the knowledge of scientific discipline. Notice that the concept of research group does not necessarily have to be represented by multiple people. For example, a post-doc who comes to a lab, a single person collaborating remotely with another group, or even a researcher who knows a field simply learning a new methodology by herself. Notice that in the latter case there is not even an external person, but two disciplines are still getting together within the same person. To facilitate the description in this series of posts, from now on we will only refer to research groups, the idea that research groups might not necessarily be more than one person being implicit.

4. From 1, 2, and 3 above, we can deduct that research innovation can happen when different research groups, carrying knowledge from different disciplines, come together to work in an area that makes use of complimentary resources from different fields.

Given that having different groups together as one of the important mechanisms to generate research innovation, the central question behind BRIL can now be raised: What is necessary for different research groups to get together and work on potentially innovative research? The assumption of BRIL is that the following two factors are necessary (but not sufficient):

1. Research groups should know about each others' resources. Knowledge about each others' resources is necessary so that they can evaluate the degree to which their fields are complimentary. This decision is important since research groups want to maximize the return on their efforts, return being defined here as the degree of research innovation.
2. Research groups should have mechanisms to assist in their decision to select areas that are more likely to generate research innovation.

Our proposal is that the description of research resources (item 1) is made possible through BRIL, while the decision support system can be created through a series of reasoning mechanisms taking information from BRIL (item 2). So, focusing on item 1: What exactly is BRIL?

BRIL can be defined as a way to map research resources from individual research groups. This mapping is created through a formal biomedical informatics approach using biomedical ontologies, which from now on we will refer simply as ontologies. The following posts will define how the BRIL ontology can be created; how it can be used in practice; and how BRIL can be used to analyze, discover and test new mechanisms of biomedical research innovation.