Constructing a Knowledge Map of Auditory Data with the Neuroscience Information Framework

Considering the state of knowledge in a particular field is always challenging, but ascertaining the gaps in knowledge may be even more difficult because recognizing what we don’t know may not be an easily definable undertaking. Knowledge expands based on the hypotheses and questions that arise from the available data produced by the collective interests of investigators in the field. Defining the syndromic space around a complex disease such as hearing loss may not neatly fall into current researcher interests, and consequently, important data may be missing. In the age of the “omes” eg., genome, more scientists are generating and using non-hypothesis driven data sets that attempt to cover the entire space in some dimension (e.g. the Allen Brain Atlas).  However, a large scale non-hypothesis driven project has not yet been undertaken in auditory science. We considered whether small-scale, hypothesis-driven, heterogenous data can be queried with a unifying protocol to probe the state of knowledge in the auditory field.  Herein we consider a method to detect gaps in the auditory data space, based on the semantic and anatomical boundaries of the auditory system, a set of knowledge that is closest to an “ome” in the auditory sphere. To accomplish this, we have enhanced the representation of anatomical entities in the NIFSTD ontology by including relationships for the auditory system and auditory system function with known anatomical regions that already contain a rich set of synonyms and partonomy relationships. We then used these anatomical regions as a set of queries to the largest collection of neuroscience data, currently over 200 databases, the Neuroscience Information Framework (http://neuinfo.org), as our representation of the data space. Data values are captured as a function of terms and databases, and plotted. The results show which anatomical regions contain a relatively rich set of data annotations and which are data-poor, suggesting relative knowledge peaks and valleys in the data space. Additionally, we will show how the auditory research community can contribute their expertise in auditory region semantics into NeuroLex (http://neurolex.org), a community knowledge platform. The NeuroLex is a wiki that contains a representation of the NIFSTD ontology, but was built specifically with scientific but not ontology expert user contributions in mind. These contributions, when properly structured by curators, will update the auditory data map automatically and the resource is then made available to the auditory community. Supported by NIH (P50-GM68762; Neuroscience Blueprint HHSN271200577531C).