Hi Bill
Just a minor clarification - the neurodevelopment ontology will not
be distinct from GO, it will be part of the GO biological process
ontology (and thus part of the OBO Foundry) and available as OWL
Cheers
Chris
On Jun 6, 2006, at 7:57 AM, William Bug wrote:
Oops -
I forgot to mention the following:
There is an upcoming meeting at the Jackson Labs (next Wed - Fri)
hosted by Judy Blake of MGI on behalf of the Gene Ontology
Consortium. The work will focus on vetting/extending a
neurodevelopment ontology they have begun to work on to be placed
in the OBO Foundary.
Hopefully, a file will be available in RDF/OWL format at the OBO
site within the next month or so.
Cheers,
Bill
On Jun 6, 2006, at 10:41 AM, William Bug wrote:
Hi All,
Sorry - I'd thought I'd already subscribed to this list, but
apparently not - until now.
The need for a mereotopologically-sound, neuroanatomical ontology
is quite pressing across the community of neuroscientists involved
in neuroinformatics projects most of which include a neuroimaging
component. Generally there is only one thing neuroscientists are
interested in when analyzing images at whatever resolution from
the macromolecular (EM) on up to the macroscopic - i.e.,
identifying biologically relevant shapes. In order for these
shapes to have any meaning in a context where one attempts to pool
data and perform relevant data reduction operations, the shapes
must exist within a shared coordinate space of some sort. For
instance, if two separate labs are examining the change in the
size of the Substantia Nigra during the course of Parkinsonian
neurodegeneration, in order for them to compare their
observations, they require several data integration/semantic
frameworks:
- a shared neuroanatomical terminology
- a shared coordinate space (to place the shapes from their
images in a comparable coordinate framework)
- a shared, well-founded anatomical ontology which encapsulates
mereotopological knowledge about shapes in - at least - 3D space.
Other knowledge resources can be helpful in supplementing this
array of tools, but, generally, these are the absolute minimum.
[NOTE: the Wikipedia has a moderately clear definition of
mereotopology (http://en.wikipedia.org/wiki/Mereotopology).
Basically, it combines a formal, ontological theory of shapes and
boundaries (mereology) with the mathematics of topology with the
goal of providing a computational formalism to support applying
logical operations to objects in space. As has been pointed out
by others, a great deal of the work in this field of applied
biomedical mereotopology derives from related work in the GIS
field. Use of mereotopology by geographers has been going on for
quite some time and is much more advanced. Work from GIS can be
adapted for use in the biomedical domain, but it must be done with
great care, as many of the assumptions behind the way researchers
represent space and manner of information being represented can
differ significantly across these disciplines.]
The same is true as you scale this problem up to field-wide
projects such as BIRN or The NeuroCommons.
As several have mentioned in this thread, there are already
existing resources that can begin to fill this need.
1) NeuroNames
Kei, Olivier, Peter Mork, and others have already given sufficient
references on NeuroNames in this thread, so that others can dig in
deeper to the specifics if they like.
Having worked with Doug Bowden, Mark Dubach, and their colleagues
over the last year or so in an advisory capacity on the specific
issue of use of NeuroNames for semantically-based, neuroanatomical
data set integration, I can add a few important qualifying points:
a) Doug et al. have been working on the extremely difficult task
of unifying neuroanatomical terminologies across mammalian species
for 20 years now. Embedded in Neuronames & Braininfo, there is a
wealth of hard won empirical knowledge related to how one achieves
this end. I think it would be ill-advised to try to duplicate
their effort, as the myriad scientific problems related to this
effort would surely present themselves again and only need to be
worked out once one.
b) Doug et al. are extremely collegial and quite receptive to
feedback and collaboration - within the bounds of their limited
resources.
c) NeuroNames is a terminological resource - not a well-founded,
spatial ontology of brain anatomy capable of supporting
mereotopological reasoning. As with most research-based
terminologies, there are many semantically-based relations
embedded in the NeuroNames graphs, but as the primary goal of NN
is to disambiguate and integrate across the neuroanatomical
lexicon, the embedded semantic information can often lead to a
logical dead end. For instance, many neuroanatomical terms
critical to specifying location in the rodent brain have been
placed in the NN category "ancillary terms," as they don't fit
into the core hierarchy in an unambiguous way. This can make use
of NN for annotating mouse brain gene & protein expression
patterns (e.g., GENSAT, the Allen Brain Atlas, various BIRN
projects) extremely problematic.
d) The NN primary structures (http://
braininfo.rprc.washington.edu/indexabout.html) provide the closest
thing to an ontology in NN. As Peter Mork pointed out, there has
been an effort in the past to unite this core NN hierarchy with
the FMA, which does provide a mereotopologically sound framework
for anatomy. Barry Smith (formal ontologist who has worked for
over a decade on problems in biomedical ontology - most
especially, though hardly exclusively, in the area of
mereotopological reasoning) and his colleagues have worked closely
with the Cornelius Rosse and his colleagues at the FMA project to
create in association with the work started in the FMA a
foundational ontology for biomedicine (the Ontology of Biological
Reality) that is becoming increasingly important to all of the
ontologies being monitored by NCBO and incorporated into the OBO
site and the emerging OBO Foundary (http://obofoundry.org/).
e) Doug and his colleagues have worked closely with Jack Park (a
consulting scientist to SRI's AI Center - http://www.ai.sri.com/)
to represent NN as a TopicMap (XTM). As many on this list may
know, there has been a moderate amount of effort to integrate and/
or reconcile XTM with RDF here at the W3C (search on "TopicMaps"
at the main RDF page - http://www.w3.org/RDF/). I'm not certain
how this effort will ultimately make NN more "semantic web"
compliant, but the bottom line is a great deal of effort has
already been expended to express NN in a semantically well-
grounded formalism.
f) Though - as Don points out - neuroanatomical representations
are likely to significantly evolve over the coming decades, as the
number of large scale gene & protein expression characterization
studies focussed on the brain continue to accumulate. Having said
that, the "conventional" view of neuroanatomy will likely remain
relevant for a long while to come, not only because it has been
used to characterize findings in the literature for the last 125+
years, but also because it did derive from a wealth of empirical
observation which is likely to remain valid in many domains of
neuroanatomical study. I would also modify Don's well informed
comment regarding the derivation of "conventional" views of
neuroanatomy. To a large extent they are related to functional
studies of the brain - as well as lesion based studies of
functional deficits dating back to the 19th century (think
"Broca's Area"), but they are also very much based on a study of
the morphology of the brain - both the external surface morphology
(sulci, gyri, and lobes), as well as histological examination of
internal structures. Many of these studies of structure in space
are likely to stay with us for some time to come (and are well-
founded in reality), though as Tim Clark & Don have pointed out in
this thread, nomenclature is still a very significant problem even
in this very "old" field.
g) licensing of NN - Doug et al. formerly had a completely open
policy to distributing NN. The only a reason a license was
instituted was at some point about 5 years back another group
sucked down the entirety of NN, reworked a lot of what was there -
probably with very practical goals directed toward making NN more
"correct" and effective in their problem domain - then
"republished" their product as "NeuroNames". This lead to a great
deal of confusion. The fact they chose to do this on sly also
meant the work they did was not necessarily compatible with the
work done by Doug et al.. In order to avoid this happening again,
it was decided a license would be established to discourage this
sort of behavior. As anyone who has developed a terminology and/
or ontology, it is absolutely essential there remain a single
curating authority, if the value of the resource is to remain in
tact. The "vetting" performed by the central authority - as is
extensively done by the curators of the Gene Ontology, for
instance - is absolutely essential to the guaranteeing the
integrity of the knowledge resource. This is not a "closed" or
proprietary process, just a highly controlled one. Unfortunately,
Doug Bowden's resources are MUCH MUCH smaller than those available
to the curators/developers of GO, so the NN curation effort
necessarily moves at a slower pace.
2) Working with the Neuroscience community
As Kei, Don, and others have stated, it would be unwise to proceed
in creating an "open source" neuroanatomical ontology without
interacting with the researchers who've already put a lot of
effort into this problem over the past decade or so. With this in
mind, I have several suggestions:
a) The 5 ways of knowing neuroanatomy:
This is a pitch I've been making which I think helps to sum up
the current ways various sub-fields have attempted to identify/
label/collate brain morphology
i) Terminlogies - e.g., NN, BrainLex
ii) Ontologies - e.g., Neuro-FMA (the project Peter Mork
referred to)
iii) Literature Informatics (CocoMac, BrainMap, NeuroScholar,
BAMS, ArrowSmith, etc.).
These are very mature projects. Some include their own
mereotopological reasoning systems (e.g., CocoMac and BrainMap) in
order to be able to pool and compare the relatedness of structures
and connectivity across different studies in the literature. The
goal in this category is to perform large-scale semantic mining of
the literature to confirm/refute current knowledge and uncover new
correlations - very much along the lines of what The NeuroCommons
Project expects to achieve via use of semantic web technologies.
Some researchers in this category are actually participating in
The NeuroCommons Project (i.e., Gully Burns, who developed
NeuroScholar).
iv) voxel/pixel analysis:
This approach applies computer vision algorithms to
automatically - or semi-automatically - identify 2D & 3D shapes in
digital anatomical images. This field is also extremely mature,
though there are many significant caveats to exactly how much of
this work can be effectively automated.
v) parameterized models:
Often these are derived from - or used to drive - the voxel/
pixel based analysis described in 'iv' - though the spatial
modeling is definitely a distinct approach from the pure voxel/
pixel approach.
None of studies you'd fit into these categories exclusively focus
on their technique/tool alone without some aspect of the other
"ways of knowing neuroanatomy" playing a role in what they do.
However, it is clear much fundamental work in this area primarily
focuses on one technique over the others.
Having said that, when the neuroscience community makes use of
this work to examine a specific biological problem, they will
often draw significant tools and resources from more than one of
these domains.
b) NCBO/NCOR sponsored meeting focused on mereotopology in
neuroanatomy:
Barry Smith is working to bring together researchers working in
the 5 domains described above. There is a very pressing need in
large-scale, field-wide neuroinformatics projects such as what is
being done in the BIRN project to have these 5 domains converge
and work more cooperatively. Right now, a lot of manual effort
has to be put out to bring them together. This is something BIRN
has been pursuing. In the last 6 months, we have received a great
deal of support and guidance on this effort from NCBO. Daniel
Rubin interacts directly with the BIRN Ontology Task Force, and
the work Barry Smith has been doing with FMA, OBO, FuGO, and PATO
have very much begun to create a much more well-founded and
computable path toward performing large-scale annotation of
neuroimaging data.
This meeting is on the NCBO/NCOR slate for 2007, but in the
interim I hope to see more effort invested in the coming year
across the 5 communities listed above toward the goal of
integrating across these "ways of knowing" now that the need has
been recognized.
3) Microarrays:
Just as Don, Kei, Alan R., and others have pointed out, high-
throughput assays - microarrays, BAC-based IHC, in situ studies
using the Gene Paint technology employed by the Allen Institute of
Brain Science to construct the Allen Brain Atlas of gene
expression in the brain - are going to transform our understanding
of neuroanatomy over the coming decades. This is just a given.
There is a pressing need to derive a means to integrate spatially-
mapped studies of gene & protein expression into a neuroimaging
setting. The spatial resolution may be very coarse - e.g., "whole
brain" - but they still provide sufficient spatial information to
be usable in the context of a neuroanatomical coordinate system.
We are working in the BIRN project to create a means for
researchers to integrate these distinct approaches to studying the
brain. As Alan R. pointed out, FuGO is working to put description
of microarray experiments on a solid, formal footing, and I would
expect one aspect of that will be to represent microarray data in
RDF/OWL. This is not a trivial problem, given as much of the
available data is merely MIAME-compliant - MIAME not even being a
data format, but just a collection of minimal data requirements.
One need only look at the great complexity of the data submission
process at the NCBI GEO site to get an appreciation for how
difficult this problem can be. A great deal of effort is being
invested in the microarray field to come up with a better means
handle this issue, and the FuGO effort will be a critical
clearinghouse for this work. The important thing to remember when
it comes to field-wide data pooling and re-analysis, it may
sometimes be necessary to get right back to the microarray primary
image files so as to reapply different criterial when performing
the statistical tests and reductions on pooled data. Given this
requirement - one we also see in the neuroimaging domain - I
believe it is very important to proceed in a well-reasoned manner
when seeking to integrate across microarray datasets using
semantic web technologies. Alan R. and myself - possibly others
too - on this list are on the FuGO Coordinators Committee, so
hopefully we can help to keep those lines of communication open.
Sorry to go on so, but this is a topic on which I've labored quite
intensively over the past year. There is a lot being done on this
issue, and I think all efforts will get much further more quickly
- and in a way that will carry more street cred with practicing
neuroscientists - if we all try to work together.
Cheers,
Bill
Bill Bug
Senior Analyst/Ontological Engineer
Laboratory for Bioimaging & Anatomical Informatics
www.neuroterrain.org
Department of Neurobiology & Anatomy
Drexel University College of Medicine
2900 Queen Lane
Philadelphia, PA 19129
215 991 8430 (ph)
610 457 0443 (mobile)
215 843 9367 (fax)
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Bill Bug
Senior Analyst/Ontological Engineer
Laboratory for Bioimaging & Anatomical Informatics
www.neuroterrain.org
Department of Neurobiology & Anatomy
Drexel University College of Medicine
2900 Queen Lane
Philadelphia, PA 19129
215 991 8430 (ph)
610 457 0443 (mobile)
215 843 9367 (fax)
Please Note: I now have a new email - [EMAIL PROTECTED]
This email and any accompany attachments are confidential. This
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