This tutorial tries to answer the following questions:
What is the best practice for ontology reuse?
Is it fine to use external ontology entities to model my local entities?
Should I import the ontologies that I reuse?
What if I only need a part of an ontology?
What if an external ontology that I reused, changes?
This tutorial tries to answer the following questions:
What is the best practice for ontology reuse?
Is it fine to use external ontology entities to model my local entities?
Should I import the ontologies that I reuse?
What if I only need a part of an ontology?
What if an external ontology that I reused, changes?
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Franz. 2014. Explaining taxonomy's legacy to computers – how and why?taxonbytes
Slides presented on the Euler/X projected (http://taxonbytes.org/prior-work-on-concept-taxonomy-2013/ & https://bitbucket.org/eulerx/euler-project) - for the conference "The Meaning of Names: Naming Diversity in the 21st Century", CU Natural History Museum, September 30, 2014.
Franz Et Al. Using ASP to Simulate the Interplay of Taxonomic and Nomenclatur...taxonbytes
Answer Set Programming (ASP) is a declarative, stable model approach to logic programming with an under-realized potential for representing and reasoning over biological information. ASP is particularly suited to address reasoning challenges with complex starting conditions and rule sets. One such challenge is the interplay of taxonomic and nomenclatural change in biological taxonomy that often results when a taxonomy is revised based on a previously published perspective. Depending on the nature of the taxonomic changes to be undertaken, one or more Code-mandated principles will apply to regulate specific and concomitant name changes. In the case of the International Code of Zoological Nomenclature, two principles of significance include the Principles of Priority and Typification. Although the relationship between the number of taxonomic and nomenclatural adjustments under a given transition scenario is not linear, the application of the name-changing rules is usually unambiguous and therefore amenable to logic representation. Here we explore the modeling of the taxonomy/nomenclature interplay in ASP with a simple, abstract nine-taxon use case that contains four terminal species of which two are type-bearers for their respective genera. Four distinct one-taxon transfer scenarios are simulated through a transition system approach, requiring 1-7 concomitant nomenclatural changes depending (1) on the priority relationships among the terminal taxa being repositioned and (2) the type-bearing name dependencies of their higher-level parents. ASP can simulate these rules faithfully and thus reason over situations that range from a one-to-one match of taxonomic and nomenclatural changes to situations where they two kinds of change become increasingly disconnected (e.g., transfer of non-type genera among tribes without name change, or "transfer" [in reverse direction] of a single priority-carrying name/taxon into a larger yet junior entity with numerous required name changes). Our results, though very preliminary, illustrate how ASP logic approach may be utilized to perform optimizations at the taxonomy/nomenclature intersection, and generally represent a novel step towards translating Code-mandated naming rules into logic, with potential benefits for virtual taxonomic domains.
Towards ubiquitous OWL computing: Simplifying programmatic authoring of and q...Hilmar Lapp
Presentation about two small tools addressing gaps commonly encountered when computing and programming with OWL (the Web Ontology Language) at scale. Given at the 2014 Bioinformatics Open Source Conference (BOSC).
The video of the talk is here: http://youtu.be/K0SlYwMyn-A
UMBEL: Subject Concepts Layer for the WebMike Bergman
This is an intro to UMBEL (Upper Mapping and Binding Exchange Layer), a lightweight ontology for relating Web content and data to a standard set of 20,000 subject concepts. Connecting to the UMBEL structure gives context and coherence to Web data. Via UMBEL, Web content, data and metadata can be linked, made interoperable, and more easily navigated and discovered. These subject concepts have defined relationships between them, and can act as semantic binding nodes for any Web content or data. The UMBEL subject concepts are derived from the OpenCyc version of the proven Cyc knowledge base.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Franz. 2014. Explaining taxonomy's legacy to computers – how and why?taxonbytes
Slides presented on the Euler/X projected (http://taxonbytes.org/prior-work-on-concept-taxonomy-2013/ & https://bitbucket.org/eulerx/euler-project) - for the conference "The Meaning of Names: Naming Diversity in the 21st Century", CU Natural History Museum, September 30, 2014.
Franz Et Al. Using ASP to Simulate the Interplay of Taxonomic and Nomenclatur...taxonbytes
Answer Set Programming (ASP) is a declarative, stable model approach to logic programming with an under-realized potential for representing and reasoning over biological information. ASP is particularly suited to address reasoning challenges with complex starting conditions and rule sets. One such challenge is the interplay of taxonomic and nomenclatural change in biological taxonomy that often results when a taxonomy is revised based on a previously published perspective. Depending on the nature of the taxonomic changes to be undertaken, one or more Code-mandated principles will apply to regulate specific and concomitant name changes. In the case of the International Code of Zoological Nomenclature, two principles of significance include the Principles of Priority and Typification. Although the relationship between the number of taxonomic and nomenclatural adjustments under a given transition scenario is not linear, the application of the name-changing rules is usually unambiguous and therefore amenable to logic representation. Here we explore the modeling of the taxonomy/nomenclature interplay in ASP with a simple, abstract nine-taxon use case that contains four terminal species of which two are type-bearers for their respective genera. Four distinct one-taxon transfer scenarios are simulated through a transition system approach, requiring 1-7 concomitant nomenclatural changes depending (1) on the priority relationships among the terminal taxa being repositioned and (2) the type-bearing name dependencies of their higher-level parents. ASP can simulate these rules faithfully and thus reason over situations that range from a one-to-one match of taxonomic and nomenclatural changes to situations where they two kinds of change become increasingly disconnected (e.g., transfer of non-type genera among tribes without name change, or "transfer" [in reverse direction] of a single priority-carrying name/taxon into a larger yet junior entity with numerous required name changes). Our results, though very preliminary, illustrate how ASP logic approach may be utilized to perform optimizations at the taxonomy/nomenclature intersection, and generally represent a novel step towards translating Code-mandated naming rules into logic, with potential benefits for virtual taxonomic domains.
Towards ubiquitous OWL computing: Simplifying programmatic authoring of and q...Hilmar Lapp
Presentation about two small tools addressing gaps commonly encountered when computing and programming with OWL (the Web Ontology Language) at scale. Given at the 2014 Bioinformatics Open Source Conference (BOSC).
The video of the talk is here: http://youtu.be/K0SlYwMyn-A
UMBEL: Subject Concepts Layer for the WebMike Bergman
This is an intro to UMBEL (Upper Mapping and Binding Exchange Layer), a lightweight ontology for relating Web content and data to a standard set of 20,000 subject concepts. Connecting to the UMBEL structure gives context and coherence to Web data. Via UMBEL, Web content, data and metadata can be linked, made interoperable, and more easily navigated and discovered. These subject concepts have defined relationships between them, and can act as semantic binding nodes for any Web content or data. The UMBEL subject concepts are derived from the OpenCyc version of the proven Cyc knowledge base.
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
Formalization and implementation of BFO 2 with a focus on the OWL implementationgolpedegato2
Formalization and implementation of Basic Formal Ontology 2 with a focus on the OWL implementation.
With an introduction to some of the underlying technologies
This is a brief description of Web semantics using OWL ontology. Ontology languages allow users to write explicit, formal conceptualizations of domain models
The main requirements are:
a well-defined syntax
efficient reasoning support
a formal semantics
sufficient expressive power
convenience of expression
Semantics is a prerequisite for reasoning support
Formal semantics and reasoning support are usually provided by mapping an ontology language to a known logical formalism using automated reasoners that already exist for those formalism
OWL is (partially) mapped on a description logic, and makes use of reasoners such as FACT and RACER
Description logic are a subset of predicate logic for which efficient reasoning support is possible.
Ideally, OWL would extend RDF Schema Consistent with the layered architecture of the Semantic Web. But simply extending RDF Schema would work against obtaining expressive power and efficient reasoning Combining RDF Schema with logic leads to uncontrollable computational properties.
Similar to Tutorial OWL and drug discovery ICBO 2013 (20)
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
2. Errata – July, 8th 2013
Here is a list of points raised during the tutorial and based on feedback
from the audience. I will try to address them for a next release of the
talk. Send me an email if you need clarification or have more
comments croset@ebi.ac.uk - Samuel
Things that can be improved (list not comprehensive):
• Direct semantics versus OWL based semantics Could be removed
from the talk. The reader can skip that.
• is_a relationship as defined by GO corresponds to a rdfs:subClassOf
axiom in OWL.
• In OWL, is_a is not an object property, it’s a built-in primitive
construct from the language defining the relashionship between
sets of things. Other properties (part-of, regulates, etc…) are defined
by OWL object properties.
4. Tutorial
• Ask questions!
• What is OWL?
• Why is it particularly interesting for life sciences?
• How to use OWL?
• What is OWL 2EL?
• How to integrate and query biomedical
knowledge?
5. Why learning OWL?
“The scientist is not a person who gives the right
answers, he's one who asks the right questions”
― Claude Lévi-Strauss
“Half of science is putting forth the right questions”
― Sir Francis Bacon
7. Why learning OWL?
“What are the human proteins that regulates
the blood coagulation?”
Classification (flat file)
Database (SQL or RDF)
Ontology (OBO)
8. Why learning OWL?
“What are the human proteins that regulates
the blood coagulation?”
What are the
parts?
What is
composing it?
What does it
even mean?
How do I
integrate the
data?
Classification (flat file)
Database (SQL or RDF)
Ontology (OBO)
9. Why learning OWL?
• Existing resources can already answer the
question But they need to interact
• Ontologies are not only labels or annotations
for biological concept (“blood coagulation”)
They help to formalize problem
• We want to mix traditional ontologies with
other large-scale data
• We want an intuitive way to formulate the
query, hiding the implementation
10. What is OWL?
• The Semantic Web: RDF URI and triples
Should improve interoperability over the Web
• Need for shared schemas ontologies
• OWL Description logics and knowledge
representation, decidable, attractive and well-
understood computational properties.
• (OWL Direct Semantics or RDF-based
semantics)
11. What is OWL?
• Confusing relations between OWL, RDF,
SPARQL, reasoning, etc…
• Here we deal with the Direct Semantics of
OWL (no RDF) It’s easier!
• You get to use the reasoner a lot!
• In OWL you build knowledge-bases or
ontologies (here these terms are synonyms –
in the wild people use the two).
12. OWL and Life Sciences
Advantages versus RDF, SQL and flat
files?
• Formal language to represent
hierarchical data
• Machine reasoning
• Large-scale (OWL 2EL)
• Knowledge integration
• Composition
• Powerful query mechanism
13. OWL 2 Terminology
• It’s all about definitions!
• Defining things based on the relations they have
• Entities: elements used to refer to real-world
objects
• Expressions: combinations of entities to form
complex descriptions from basic ones
• Axioms: the basic statements that an OWL
ontology expresses Pieces of knowledge
http://www.w3.org/TR/owl2-primer/#Modeling_Knowledge:_Basic_Notions
14. Entities
• Classes: Categories and Terminology
– Protein, Human, Drug, Chemical, P53, Binding site,
etc… Pretty much everything in life science.
• Individuals (objects): Instances
– Rex the dog, this mouse on the bench, you, etc…
• Properties: Relations between individuals
– Part of, regulates, perturbs, etc…
15. Axioms
• Statements, pieces of knowledge express the
truth.
• How classes and properties relate to each other:
– All Humans are Mammals Human is a subclass of
Mammal
• You should always think in terms of individuals.
In biology we don’t really deal much with real
individuals, yet classes/properties and axioms are
built from relationships between anonymous
individuals.
• Our first OWL axiom: SubClassOf
16. Ontology/Knowledge-base
• Set of axioms
• Serialized as “.owl” file – Here using the Manchester
syntax (Description logics semantics)
• Example of output (look at the format, don’t try to
understand the logic now):
ObjectProperty: part-of
Class: owl:Thing
Class: Cell
Class: Nucleus
SubClassOf:
part-of some Cell
20. Exercise 1 – Classes and axioms
• Open the file “NCBI-taxonomy-mammals.owl”
with a text editor. Can you understand what’s
inside?
• Now open the file with Protégé and go under
the tab “classes”. You can use the option
“render by label” in the “View” menu.
• Can you recognize the classes? What do they
describe?
• Can you spot the axioms? What do they
capture?
21. Reasoner
• A program that understand the axioms and
can deduce things from it.
• Used to classify the ontology.
• Query engine for knowledge-bases.
• More or less fast depending on the number
and type of axioms.
22. Exercise 2 - Reasoning
• In Protégé, go under the “DL query” tab and
retrieve all descendant classes of the class
Abrothrix (or NCBI_156196).
• What does this query means? What about the
results?
24. Constructs – Class expressions
• Combining classes and properties to define
more things (class expression) Composition
• Intersection: and
– Mammal and Omnivore
• Existential Restriction: some
– part-of some Cell Cuneiform script
(3000 BC):
Head
Food
Eathttp://en.wikipedia.org/wiki/Cuneiform
26. Constructs & axioms
Human SubClassOf Mammal and Omnivore
Pig
HumanMammal
Omnivore
Mammal and Omnivore
individual
27. Constructs & axioms
This definition (Mammal and Omnivore) of the
concept “Human ” is partial.
• Every human must be at least a mammal and
an omnivore according to our definition.
• But it’s not because you are a mammal and an
omnivore that you are necessary human!!
Human SubClassOf Mammal and Omnivore
28. Construct: some
Existential restriction: Weird construct at first, but
useful while dealing with incomplete knowledge
P some C: if it exists then a least one instance of C linked by P
Cell
part-of some Cell
part-of
part-of
part-of
29. Constructs & axioms
Cell
part-of some Cell
part-of
part-of
part-of
Nucleus
Nucleus SubClassOf part-of some Cell
“Each nucleus must be part of a cell”
part-of
30. Exercise 3 – Implementing the axiom
• Create a new project inside Protégé.
• Implement “Human SubClassOf Mammal and
Omnivore”
• Run the reasoner and look at the hierarchy of
classes. Does it make sense?
• That’s the main role of the reasoner
classifying things based on their definitions.
• “Conceptual Lego”
33. Real-life example: The Gene Ontology
• Open Biomedical Ontology (OBO) format
originally.
• Moved to OWL Stronger semantics
http://www.geneontology.org/GO.ontology-ext.relations.shtml
CellNucleus
part-of
34. GO constructs
• Central pattern:
A SubClassOf P some B
Nucleus SubClassOf part-of some Cell
http://www.geneontology.org/GO.ontology-ext.relations.shtml
CellNucleus
part-of
( )
37. Exercise 4 – Transitive property
• Open the “gene_ontology.owl” file.
• What are the things that are a
biological_process and part_of some 'wound
healing‘ ?
• Look at the class “blood coagulation, common
pathway”. Is it obvious for this class to be in
the results?
39. Exercise 5 – Chained properties
• Look at the “regulates” property inside
Protégé.
• What are the things that are a
biological_process and regulates some 'mitotic
cell cycle’ ?
• Look at the class “positive regulation of
syncytial blastoderm mitotic cell cycle”
• Is it obvious for this class to be in the results?
40. GO – Rbox: positively/negatively regulates
SubProperty
41. Exercise 6 – Sub Properties
• Look at the “positively-regulates” property
inside Protégé.
• What are the things that are a
biological_process and positively_regulates
some 'mitotic cell cycle' ?
• Are they different from the things that are
biological_process and regulates some 'mitotic
cell cycle'?
42. Exercise 7 – Verifying properties
• Are we respecting the GO specifications?
43. Summary GO
• Concepts are defined using one construct only
(A SubClassOf P some B).
• Rich RBox
• OWL is helpful to represent these relations,
helps to abstract away.
44. Knowledge integration
• We would like to answer questions over all
different source of knowledge.
• “Thrombosis is a widespread condition and a
leading cause of death in the UK.”
• We would like to find a new protein target in
order to treat thrombosis.
• Here we would like to know “what are the
human proteins that regulates the blood
coagulation”.
46. Exercise 8 – Integrating knowledge
• Open the file uniprot.owl
• Do you understand its content? Look for the class
“Protein”
• Now open the file “integrated.owl”
• How would you formulate the question “what are
the human proteins that regulates the blood
coagulation” in OWL?
• involved_in some (regulates some ‘blood
coagulation’) and expressed_in some ‘Homo
sapiens’
47. Implementation using Brain
Brain brain = new Brain();
brain.learn("data/gene_ontology.owl");
brain.learn("data/NCBI-taxonomy-mammals.owl");
brain.learn("data/uniprot.owl");
String query = "involved_in some (regulates some GO_0007596) and
expressed_in some NCBI_9606“;
List<String> subClasses = brain.getSubClasses(query,false);
brain.sleep();
48. Large-scale implementation
• OWL is computing intensive OWL 2EL
• Less axioms and constructs easier for you
to remember and easier for the reasoner to
compute
• Suited for life sciences lots of classes, few
instances
49. H2O H HO
Expressivity
RDF
SPARQL RDFS OWL2OWL2 EL
PSPACE
(all constructs)
PTIME PTIME
N2EXPTIME-
complete
LOGSPACE
(AND, FILTER)
NP
(AND, FILTER,
UNION)
Tractable
Parallelism
http://www.w3.org
/TR/owl2-profiles/
50. Why learning OWL?
“What are the human proteins that regulates
the blood coagulation?”
What are the
parts?
What is
composing it?
What does it
even mean?
How do I
integrate the
data?
Classification (flat file)
Database (SQL or RDF)
Ontology (OBO)
51. Conclusion
• Ask questions!
• What is OWL?
• Why is it particularly interesting for life sciences?
• How to use OWL?
• What is OWL 2EL?
• How to integrate and query biomedical
knowledge?
52. Thank you!
• croset@ebi.ac.uk
• More questions: StackOverflow (tag “OWL”)
• If you think things could be improved please
send feedback, fork or contribute