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