how do we know what we don’t know: using the neuroscience information framework to reveal...
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Presentation at Tools for Integrating and Planning Experiments in Neuroscience-UCLA March 11, 2014TRANSCRIPT
How do we know what we don’t How do we know what we don’t know: Using the Neuroscience know: Using the Neuroscience
Information Framework to reveal Information Framework to reveal knowledge gapsknowledge gaps
Maryann E. Martone, Ph. D.University of California, San Diego
Tools for Integrating and Planning Experiments in Neuroscience-UCLA March 11, 2014
We say this to each other all the time, but we set up systems for scholarly advancement and communication that are the antithesis of integration
Whole brain data (20 um
microscopic MRI)
Mosiac LM images (1 GB+)
Conventional LM images
Individual cell morphologies
EM volumes & reconstructions
Solved molecular structures
No single technology serves these all equally well.Multiple data types;
multiple scales; multiple databases
A data integration problemA data integration problem
• NIF is an initiative of the NIH Blueprint consortium of institutesNIF is an initiative of the NIH Blueprint consortium of institutes– What types of resources (data, tools, materials, services) are available to the What types of resources (data, tools, materials, services) are available to the
neuroscience community?neuroscience community?– How many are there?How many are there?– What domains do they cover? What domains do they not cover?What domains do they cover? What domains do they not cover?– Where are they?Where are they?
• Web sitesWeb sites• DatabasesDatabases• LiteratureLiterature• Supplementary materialSupplementary material
– Who uses them?Who uses them?– Who creates them?Who creates them?– How can we find them?How can we find them?– How can we make them better in the future?How can we make them better in the future?
http://neuinfo.org
• PDF filesPDF files
• Desk drawersDesk drawers
Old Model: Single type of content; single Old Model: Single type of content; single mode of distributionmode of distribution
ScholarScholar
LibraryLibrary
Scholar
PublisherPublisher
Systems for cataloging, standards, and citation in placeSystems for cataloging, standards, and citation in place
Scholar
Consumer
Libraries
Data Repositories
Code Repositories
Community databases/platforms
OA
Curators
Social Networks
Social Networks
Social Networks
Social NetworksSocial
NetworksSocial
Networks
Peer Reviewers
NarrativeNarrative
WorkflowsWorkflows
DataData
ModelsModels
MultimediaMultimedia
NanopublicationsNanopublications
CodeCode
The duality of modern scholarship
Observation: Those who build information systems from the machine side don’t understand the requirements of the human very well
Those who build information systems from the human side, don’t understand requirements of machines very well
Scholarship requires the ability to cite and track usage of scholarly artifacts. In our current mode of working, there is no way to track artifacts as they move through the ecosystem; no way to incrementally add human expertise; no way to look across the entirety
Scholarship requires the ability to cite and track usage of scholarly artifacts. In our current mode of working, there is no way to track artifacts as they move through the ecosystem; no way to incrementally add human expertise; no way to look across the entirety
Whither neuroscience information?Whither neuroscience information?
∞
What is easily machine processable and accessible
What is easily machine processable and accessible
What is potentially knowableWhat is potentially knowable
What is known:Literature, images, human
knowledge
What is known:Literature, images, human
knowledge
Unstructured; Natural language processing, entity recognition, image
processing and analysis; paywalls communication
Abstracts vs full text vs tables etc
NIF: A New Type of Entity for New Modes of NIF: A New Type of Entity for New Modes of Scientific DisseminationScientific Dissemination
NIF: A New Type of Entity for New Modes of NIF: A New Type of Entity for New Modes of Scientific DisseminationScientific Dissemination
• NIF’s mission is to maximize the awareness of, access to and utility of research resources produced worldwide to enable better science and promote efficient use– NIF unites neuroscience information without respect to
domain, funding agency, institute or community– NIF is like a “Pub Med” for all biomedical resources and a “Pub
Med Central” for databases– Makes them searchable from a single interface– Practical and cost-effective; tries to be sensible– Learned a lot about the effective data sharing
The Neuroscience Information Framework is an initiative of the NIH Blueprint consortium of institutes http://neuinfo.orgThe Neuroscience Information Framework is an initiative of the NIH Blueprint consortium of institutes http://neuinfo.org
Surveying the resource Surveying the resource landscapelandscape
Data Federation: Deep searchData Federation: Deep search
http://neuinfo.orgWith the thousands of databases and other information sources available, simple descriptive metadata will not sufficeWith the thousands of databases and other information sources available, simple descriptive metadata will not suffice
A unified framework for neuroscienceA unified framework for neuroscience
Hippocampus OR “Cornu Ammonis” OR “Ammon’s horn”
Hippocampus OR “Cornu Ammonis” OR “Ammon’s horn”
NIF queries > 200 databases; ~400 million recordsNIF queries > 200 databases; ~400 million records
NIF Semantic Framework: NIFSTD ontologyNIF Semantic Framework: NIFSTD ontology
• NIF uses ontologies to help navigate across and unify neuroscience resources
• Ontologies are built from community ontologies cross integration with other domains
NIFSTDNIFSTD
OrganismOrganism
NS FunctionNS FunctionMoleculeMolecule InvestigationInvestigationSubcellular structure
Subcellular structure
MacromoleculeMacromolecule GeneGene
Molecule DescriptorsMolecule Descriptors
TechniquesTechniques
ReagentReagent ProtocolsProtocols
CellCell
ResourceResource InstrumentInstrument
DysfunctionDysfunction QualityQualityAnatomical Structure
Anatomical Structure
PurkinjeCell
AxonTerminal
Axon DendriticTree
DendriticSpine
Dendrite
Cell body
Cerebellarcortex
Bringing knowledge to data: Ontologies as frameworkBringing knowledge to data: Ontologies as framework
There is little obvious connection between data sets taken at different scales using different microscopies without an explicit representation of the biological objects that the data represent
There is little obvious connection between data sets taken at different scales using different microscopies without an explicit representation of the biological objects that the data represent
: C: C
Neurolex: > 1 million triples
Dr. Yi Zeng: Chinese neural knowledge baseNIF Cell Graph
This is your brain on computers
Ontologies as a data integration frameworkOntologies as a data integration framework
•NIF Connectivity: 7 databases containing connectivity primary data or claims from literature on connectivity between brain regions
•Brain Architecture Management System (rodent)•Temporal lobe.com (rodent)•Connectome Wiki (human)•Brain Maps (various)•CoCoMac (primate cortex)•UCLA Multimodal database (Human fMRI)•Avian Brain Connectivity Database (Bird)
•Total: 1800 unique brain terms (excluding Avian)
•Number of exact terms used in > 1 database: 42•Number of synonym matches: 99•Number of 1st order partonomy matches: 385
01-10
11-100>101
Open World-Closed World: Mapping the knowledge - data space
Data Sources
NIF lets us ask: where isn’t there data? What isn’t studied? Why?NIF lets us ask: where isn’t there data? What isn’t studied? Why?
ForebrainForebrain
MidbrainMidbrain
HindbrainHindbrain
01-10
11-100>101
Neuroimaging Data-Knowledge Space?Data Sources
““The Data Homunculus”The Data Homunculus”
Funding drives representation in the data spaceFunding drives representation in the data space
Neurolex.org: A computable Neurolex.org: A computable lexicon for neurosciencelexicon for neuroscience
http://neurolex.org Larson et al, Frontiers in Neuroinformatics, 2013Larson et al, Frontiers in Neuroinformatics, 2013
•Semantic MediaWiki•Provide a simple interface for defining the concepts required
•Light weight semantics
•Community based:•Anyone can contribute their terms, concepts, things
•Anyone can edit
•Anyone can link
•Accessible: searched by Google•Growing into a significant knowledge base for neuroscience•25,000 concepts
Demo D03
200,000 edits150 contributors
200,000 edits150 contributors
Neurolex Structural Lexicon: Defining brain Neurolex Structural Lexicon: Defining brain partsparts
Structural LexiconStructural LexiconThe scourge of neuroanatomical nomenclatureThe scourge of neuroanatomical nomenclature
• Problem: Neuroscientists have a myriad number of ways to parcellate the brain– Brains are made up of networks
that do not respect gross anatomical boundaries
– Partonomies are generally along multiple axes:• Volummetric (species
dependent): NeuroNames• Functional (Swanson)• Developmental• Cytoarchitectural
– Partonomies are often weak• Arbitrary but defensible
Program on Ontologies for Neural Structures, INCF-creating a computable lexicon for neural structuresProgram on Ontologies for Neural Structures, INCF-creating a computable lexicon for neural structures
Neuroanatomy without bordersNeuroanatomy without borders
Brainmaps.org
Structural Lexicon in NeurolexStructural Lexicon in Neurolex
Brain RegionBrain
RegionBrain ParcelBrain Parcel
•Trans-species•“Stateless”, i.e. no universal defining criteria•General structures and partonomies based on Neuroanatomy 101
Partially overlaps
e.g., Hippocampus, Dentate gyrus
•Species specific•Specific reference•Defining criteria•Sometimes partonomy; sometimes not
e.g., Hippocampus of ABA2009
““When I use a word...it means what I choose it When I use a word...it means what I choose it to mean”to mean”
Neurolex NeuronNeurolex Neuron
• Led by Dr. Gordon Shepherd
• > 30 world wide experts
• Simple set of properties
• Consistent naming scheme
• Integrated with Structural Lexicon
• Used for annotation in other resources, e.g., NeuroElectro
““You have broken links”You have broken links”
Red Links: Information is missing (or misspelled)Red Links: Information is missing (or misspelled)
Location of Cell Soma
Location of dendrites
Location of local axon arbor
Analysis of Red Links in the Neuron RegistryAnalysis of Red Links in the Neuron Registry
• Analysis of red links tells us where instructions aren’t clear, the information isn’t available, or the model insufficient– Conceptualization not
clear• what is most important
thing about local axon terminals?
– Tool doesn’t capture all details
Social networks and community sites let us learn things from the collective behavior of contributors INCF/HBP Knowledge SpaceSocial networks and community sites let us learn things from the collective behavior of contributors INCF/HBP Knowledge Space
Re-inventing Narrative: Do I have to write in Re-inventing Narrative: Do I have to write in triples?triples?
• Not all entities are well-enough specified that they lend themselves to deep annotation– And, as we’ve seen in the previous example, we probably
don’t want to pretend that they are
• But…sometimes they are– Semantic annotation of research papers to make them
“machine-interpretable” has been a goal of many– Can we update the way that authors produce manuscripts
so that they are easier to process?
• NIF pilot project: Semantic annotation of entities that researchers would understand
The problem: How many papers were published that used my: antibody
Paz et al, J Neurosci, 2010
Now, go find the antibody
http://www.millipore.com/searchsummary.do?tabValue=&q=gfap Nov 12, 2010
Jan 15, 2014A catalog number is not a persistent identifierA catalog number is not a persistent identifier
The problem
is general across
multiple resource
types and
disciplines
The problem
is general across
multiple resource
types and
disciplines
Vasilevsky et al, Peer J 2013Vasilevsky et al, Peer J 2013
If we can’t do it, neither can the robot
• Automated text mining tools were not deployed on this problem, because too few antibodies were able to be automatically identified
• We are asking authors to change their ways, instead!
• Almost all antibodies were identified with the company name, city and state, but the information is useless if the goal is to identify the antibody used
The Resource Identification InitiativeThe Resource Identification Initiative
• NIF, FORCE11 and partners– Led by Anita
Bandrowski and Melissa Haendel
• Identify 3 types of research resources– Antibodies– Genetically
modified animals– Software
http://force11.org/Resource_identification_initiativehttp://force11.org/Resource_identification_initiative
Musings: You can’t do that!Musings: You can’t do that!• Two powerful trends in the 21st century:
– Networking machines and networking people– Moving science into a machine-accessible platform has been a challenge
• Mechanistically• Culturally• Sociologically
• “A foolish consistency is the hobgoblin of little minds”– When you have a lot of data and information in an accessible form, we can start to look
at actual practices and trends– Focusing on the “negative space”, i.e., what we don’t know, reveals glimpses into sources
of bias and confusion• When we scratch the surface of science, we find uncertainty and confusion
– Not a failure, but an opportunity
• Sometimes we can be precise, i.e., which reagents we used• Sometimes, we can’t so we should set up systems so we can learn from that
•
Next Steps: Neurolex to Knowledge SpaceNext Steps: Neurolex to Knowledge Space
Data SpaceData Space
Laboratory Space
Laboratory Space
Knowledge Space
Knowledge Space
BAMS
LexiconLexicon
EncyclopediaEncyclopedia
Anatomist Anatomist Informaticist Informaticist
What is the “completeness” of our knowledge?What is the “completeness” of our knowledge?
Neocortex
Olfactory bulb
Neostriatum
Cochlear nucleus
All neurons with cell bodies in the same brain region are grouped togetherAll neurons with cell bodies in the same brain region are grouped together
Properties in Neurolex
•Simple set of properties that can be reasonably supplied with a minimal amount of effort
The case of the meanest journal in the world, coincidentally having the lowest retraction rate
The landscape is messy, diverse and evolving: Data to The landscape is messy, diverse and evolving: Data to Knowledge – Knowledge to DataKnowledge – Knowledge to Data
NIF favors a hybrid, tiered, federated system
• Domain knowledge– Ontologies
• Claims, models and observations– Virtuoso RDF triples – Model repositories
• Data– Data federation– Spatial data– Workflows
• Narrative– Full text access
NeuronNeuron Brain partBrain part DiseaseDiseaseOrganismOrganism GeneGene
Caudate projects to Snpc
Caudate projects to Snpc Grm1 is upregulated
in chronic cocaineGrm1 is upregulated
in chronic cocaineBetz cells
degenerate in ALSBetz cells
degenerate in ALS
NIF provides the tentacles that connect the pieces: a new type of entity for 21st century scienceNIF provides the tentacles that connect the pieces: a new type of entity for 21st century science
TechniqueTechniquePeoplePeople
Data about the subthalamusData about the subthalamus
http://neuinfo.org