Post on 24-Feb-2016
description
Mental Functioning and Semantic Search in the Neuroscience Information Framework
Maryann MartoneFahim Imam
Funded in part by the NIH Neuroscience Blueprint HHSN271200800035C via NIDA
Neuroscience Information Framework – http://neuinfo.org
The Neuroscience Information Framework: Discovery and utilization of web-based resources for neuroscience
• A portal for finding and using neuroscience resources
A consistent framework for describing resources
Provides simultaneous search of multiple types of information, organized by category
Supported by an expansive ontology for neuroscience
Utilizes advanced technologies to search the “hidden web”
http://neuinfo.org
UCSD, Yale, Cal Tech, George Mason, Washington Univ
Supported by NIH Blueprint
Literature
Database Federation
Registry
NIF takes a global view of resources• NIF’s goal: Discover and use resources
– Data– Databases– Tools– Materials– Services
• Federated approach: Resources are developed and maintained by the community– >150 data sources; 350M records
• Agile approach: the NIF system is designed to be populated quickly and allow for incremental improvements to representation and search– Contract specifies 25 sources/year
NIF’s Rules for using digital
resources#1: YOU HAVE TO FIND THEM!!!!!!!
#2: You have to access/open them
#3: You have to understand them
Neuroscience is inherently interdisciplinary; no one technique reveals all
What do you mean by data?Databases come in many shapes and sizes• Primary data:
– Data available for reanalysis, e.g., microarray data sets from GEO; brain images from XNAT; microscopic images (CCDB/CIL)
• Secondary data– Data features extracted through data
processing and sometimes normalization, e.g, brain structure volumes (IBVD), gene expression levels (Allen Brain Atlas); brain connectivity statements (BAMS)
• Tertiary data– Claims and assertions about the meaning
of data• E.g., gene
upregulation/downregulation, brain activation as a function of task
• Registries:– Metadata– Pointers to data sets or materials
stored elsewhere• Data aggregators
– Aggregate data of the same type from multiple sources, e.g., Cell Image Library ,SUMSdb, Brede
• Single source– Data acquired within a single
context , e.g., Allen Brain Atlas
• Set of modular ontologies – 86, 000 + distinct concepts +
synonyms• Expressed in OWL-DL language
– Supported by common DL Reasoners
– Currently supports OWL 2• Closely follows OBO community best
practices • Avoids duplication of efforts
– Standardized to the same upper level ontologies • e.g., Basic Formal Ontology
(BFO), OBO Relations Ontology (OBO-RO)
– Relies on existing community ontologies • e.g., CHEBI, GO, PRO, DOID,
OBI etc.
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• Modules cover orthogonal domain e.g. , Brain Regions, Cells, Molecules,
Subcellular parts, Diseases, Nervous system functions, etc.
Bill Bug et al.
NIFSTD Ontologies
Neuroscience Information Framework – http://neuinfo.org
Importing into NIFSTD• NIF converts to OWL and aligns to BFO, if not already
– Facilitates ingestion, but can have negative consequences for search if model adds computational complexity• Data sources do not make careful distinctions but use what is customary for
the domain
• Modularity: NIF seeks to have single coverage of a sub-domain– We are not UMLS or Bioportal
• NIF uses MIREOT to import individual classes or branches of classes from large ontologies– NIF retains identifier of source
• NIF uses ID’s for names, not text strings– Avoids collision– Allows retiring of class without retiring the string
NIFSTD has evolved as the ontologies have evolved; had to make many compromises based on ontologies and tools available
NIFSTD Modules and SourcesNIFSTD Modules External Source Import/ AdaptOrganismal taxonomy NCBI Taxonomy, GBIF, ITIS, IMSR, Jackson Labs mouse catalog; the model
organisms in common use by neuroscientists are extracted from NCBI Taxonomy and kept in a separate module with mappings
Adapt
Molecules,Chemicals
IUPHAR ion channels and receptors, Sequence Ontology (SO); NIDA drug lists from ChEBI, and imported Protein Ontology (PRO)
Adapt/Import
Sub-cellular anatomy Sub-cellular Anatomy Ontology (SAO). Extracted cell parts and subcellular structures from SAO-CORE. Imported GO Cellular Component with mapping.
Adapt/Import
Cell CCDB, NeuronDB, NeuroMorpho.org. Terminologies; OBO Cell Ontology was not considered as it did not contain region specific cell types
Adapt
Gross Anatomy NeuroNames extended by including terms from BIRNLex, SumsDB, BrainMap.org, etc; Multi-scale representation of Nervous System, Macroscopic anatomy
Adapt
Nervous system function
BIRN, BrainMap.org, MeSH, and UMLS , GO Biological functions Adapt
Nervous system dysfunction
Nervous system disease from MeSH, NINDS terminology; Imported Disease Ontology (DO) with mapping
Adapt/Import
Phenotypic qualities Phenotypic Quality Ontology (PATO); Imported as part of the OBO foundry core
Import
Investigation: reagents Overlaps with molecules above from ChEBI, SO, and PRO Adapt/Import
Investigation: instruments, protocols, plans
CogPo, BIRNLex Adapt
Investigation: resource type
NIF, OBI, NITRC, Biomedical Resource Ontology (BRO) Adapt
Biological Process Gene Ontology (GO) biological process Import
Neuroscience Information Framework – http://neuinfo.org
What are the connections of the hippocampus?
Hippocampus OR “Cornu Ammonis” OR “Ammon’s horn” Query expansion: Synonyms
and related conceptsBoolean queries
Data sources categorized by
“data type” and level of nervous
system
Common views across multiple
sources
Tutorials for using full resource when getting there from
NIF
Link back to record in
original source
Entity mapping
BIRNLex_435 Brodmann.3
Explicit mapping of database content helps disambiguate non-unique and custom terminology
• Search Google: GABAergic neuron• Search NIF: GABAergic neuron
– NIF automatically searches for types of GABAergic neurons
– Defined by OWL axioms
Types of GABAergic neurons
NIF Concept-Based Search
Neuroscience Information Framework – http://neuinfo.org
Ontological Query expansion through OntoQuestExample Query Type Ontological ExpansionA single term query for Hippocampus and its synonyms
synonyms(Hippocampus); expands to Hippocampus OR "Cornu ammonis" OR "Ammon's horn" OR "hippocampus proper".
A conjunctive query with 3 terms transcription AND gene AND pathwayA 6-term AND/OR query with one term expanded into synonyms
(gene) AND (pathway) AND (regulation OR "biological regulation") AND (transcription) AND (recombinant)
A conjunctive query with 2 terms, where a user chooses to select the subclasses of the 2nd term
synonyms(zebrafish AND descendants(promoter,subclassOf))), zebrafish gets expanded by synonym search and the second term transitively expands to all subclasses of promoter as well as their synonyms.
A single term query for an anatomical structure where a user chooses to select all of the anatomical parts of the term along with synonyms
synonyms(descendants(Hippocampus,partOf)),expands to all parts of hippocampus and all their synonyms through the ontology. All parts are joined as an “OR” operation.
A conjunctive query with 2 terms, where a user chooses to select all the equivalent terms for the 2nd term
synonyms(Hippocampus) AND equivalent(synonyms(memory)), the second term uses the ontology to find all terms that are equivalent to the term memory by ontological assertion, along with synonyms.
A conjunctive query with 2 terms, where a user is interested in a specific subclasses for both of the terms
synonyms(x:descendants(neuron,subclassOf) where x.neurotransmitter='GABA') AND synonyms(gene where gene. name='IGF'), x is an internal variable.
A query to seek all subclasses of neuron whose soma location is in any transitive part of the hippocampus
synonyms(x:descendants(neuron,subclassOf) where x.soma.location = descendants (Hippocampus, partOf))
A query to seek a conceptual term that is semantically equivalent to a collection of terms rather than a single term.
'GABAergic neuron' AND Equivalent ('GABAergic neuron'), The term is recognized as ontologically equivalent to any neuron that has GABA as a neurotransmitter and therefore expands to a list of inferred neuron types OntoQuest – NIF’s ontology management system for NIFSTD ontologies
• Implements various graph search algorithms for ontological graphs•Automated query expansion for NIFSTD terms, including the ones with defined logical restrictions.
Gupta et al., 2010
NIF information spaceNIF developed a tiered system• Domain knowledge
– What you would teach someone coming into your domain• NIFSTD/Ontoquest• All upper level BFO categories are
suppressed
• Claims based on data– Bridge files across domains
(constructed by NIF), Databases, triple stores,
– Text• Data
– Relational databases– Spreadsheets
Concepts
DataKnowledgeBase
Concepts, Entities + data summaries
Scientists search via the terms they use, not what we would like them to use-NIF needs a broad net to find relevant resources
When searching across broad information sources, need to search for what people are looking for
What genes are upregulated by drugs of abuse in the adult mouse?
Gene upregulated mice illegal drug
NIF “translates” common concepts through ontology and annotation standards
• What genes are upregulated by drugs of abuse in the adult mouse?
MorphineIncreased expression
Adult Mouse
Arbitrary but defensible
NIFSTD AND NEUROLEX WIKI
• Semantic wiki platform• Provides simple forms for
structured knowledge• People can add concepts,
properties, and annotations• Generate hierarchies without
having to learn complicated ontology tools
• Community can contribute– Relax rules for NIFSTD so
dedicated domain scientists can contribute their knowledge and review other contributions
– Teaches structuring of knowledge via red links/blue links
– Process is tracked and exposed– Implemented versioning
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Larson et al.
Readily indexed by Google; queries to NIF data via NIF navigator
NeuroLex Content Structure
Stephen D. Larson et al.
Neurolex is becoming a significant knowledge base
Top Down Vs. Bottom upTop-down ontology construction• A select few authors have write privileges• Maximizes consistency of terms with each other• Making changes requires approval and re-publishing• Works best when domain to be organized has: small corpus, formal categories, stable entities, restricted entities, clear edges.• Works best with participants who are: expert catalogers, coordinated users, expert users, people with authoritative source of judgment
Bottom-up ontology construction• Multiple participants can edit the ontology instantly• Semantics are limited to what is convenient for the domain• Not a replacement for top-down construction; sometimes necessary to increase flexibility• Necessary when domain has: large corpus, no formal categories, no clear edges• Necessary when participants are: uncoordinated users, amateur users, naïve catalogers• Neuroscience is a domain that is less formal and neuroscientists are more uncoordinated
Larson et. al
NIFSTD
NEUROLEX
Neuroscience Information Framework – http://neuinfo.org
Engaging domain scientists
Memory
Mental Process
Cognitive process
Recall
Retrieval
Encoding
Disposition Planned process Continuant
Episodic Non-declarative
Mental state
? ? ?
Mental functioning is difficult to define and dissect
•Very few behaviors are “pure”• Operationally defined
through experiments
•What is a mental function?• Activity, state, function,
process
•Subtypes are rarely disjoint• Episodic memory• Semantic memory• Procedural memory• Declarative memory
•Distinctions among paradigms, assessments, tests, rating scales, tasks are often subtle
Early work done in BIRN; later terms added by students and curators
Neurolex does not adhere strictly to BFO
Concepts and things happily co-exist; content gets reconciled over time
Nevertheless...• We do not allow
duplicates• We do not allow
multiple inheritance– Use “role” to shortcut
many relations• We do try to re-factor
contributions so as to avoid collisions across our domains
• But...once they are in the wiki, they will move about and be added to as necessary
Neuinfo.org/neurolex/wiki/COGPO_00123
Cognitive-related searches through NIF
• fear prefrontal arousal• Attention and distraction• Passive viewing• stroop effect• sequence learning• studies done on the cognitive-
behavioral model of addiction• memory recall• self-administration• Visual oddball paradigm• Sexual Orientation
•Face recognition•neurophysiology of language•Olfaction•Consciousness•Gustatory
Scientists tend to focus on tests and general concepts rather than deep considerations of cognitive processes
Mental Functioning: What NIF needs
• Computable taxonomies of test (assessments, paradigms, tasks) types– Test types should be related to the function they purport to
measure but will only be an approximation– Not just human!!!
• Computable operational definitions of cognitive concepts– Translates tests into concepts used in search– Dementia rating scale scores = Dementia– Smoking assessment scores = smoker
Concluding Remarks• NIFSTD is utilized to provide a semantic index to heterogeneous
data sources– BFO allows us to promote a broad semantic interoperability between
biomedical ontologies. – The modularity principles allows us to limit the complexity of the
base ontologies• NIF defines a process to form complex semantics to
neuroscience concepts through NIFSTD and NeuroLex collaborative environment. – NIF encourages the use of community ontologies
• Moving towards building rich knowledgebase for Neuroscience that integrates with larger life science communities
Neuroscience Information Framework – http://neuinfo.org
Points of DiscussionCogPO/CogAT/NEMO/MHO Harmonization?• What kind of interplay are we looking at? • Is it about re-use of ontological vocabularies?• What should be the best practice for reuse?
– Re-using URI vs Creating new class and Mapping– Non-semantic reuse of classes as entities (e.g., MIREOT)
• Is it about building new relationships between the entities covered in all these four ontologies?– What do we achieve through doing this?
• Are we trying to connect all the curated/ annotated experimental data-set to a common semantic layer?
• All of the above?
What should be NIF's role?• How can we help to expose your experiments and results to a broader audience through our
interface? • What kind of involvement can people have in terms of re-using your ontological content or
contributing to your content?• We want to be the 'host' of all the NS concepts and entities, but not necessarily the 'maintainer'.
What ontology isn’t(or shouldn’t be)
• A rigid top-down fixed hierarchy for limiting expression in the neurosciences– Not about restricting expression but
how to express meaning clearly and in a machine readable form
• A bottomless resource-eating pit that consumes dollars and returns nothing
• A cure-all for all our problems• A completely solved area
– Applied vs theoretical• Easy to understand
Mike Bergman