SlideShare una empresa de Scribd logo
1 de 27
Descargar para leer sin conexión
Introduction                                 Sequences                              Summary and conclusions




                          A formal ontology of sequences

                   Robert Hoehndorf1,2 , Janet Kelso2 , Heinrich Herre1

                                          1 Institute
                                                 for Informatics
                                                 and
                Research Group Ontologies in Medicine, Institute for Medical Informatics,
                           Statistics and Epidemiology, University of Leipzig
               2 Department   of Evolutionary Genetics, Max-Planck-Institute for Evolutionary
                                                Anthropology
Introduction                         Sequences                     Summary and conclusions



Ontology




               ontology (philosophy) studies the nature of existence and
               categories of being
               an ontology (computer science) is the “explicit specification of
               a conceptualization of a domain” [Gruber, 1993]
               specifies the meaning of terms in a vocabulary
Introduction                           Sequences                         Summary and conclusions


Ontology
Explicit definitions




       P(x1 , ..., xn ) ⇐⇒ φ(x1 , ..., xn )
               φ does not contain P (non-circular definition), but predicates
               P1 , ..., Pm .
               every occurrence of P(x1 , ..., xn ) can be replaced with
               φ(x1 , ..., xn )
               φ(x1 , ..., xn ) contains undefined terms P1 , ..., Pm .
               define P1 , ..., Pm using terms Q1 , ..., Qr , etc.
               set of primitive (undefined) terms remaining
               every statement can be expressed using only primitive terms
Introduction                        Sequences                       Summary and conclusions


Ontology
Axiomatic method




               construct statements using only primitive terms
               select set of statements T as true within a domain
               select set of axioms Ax ⊆ T
               deductively infer true statements from the axioms
               logical axioms always hold: ¬¬p → p, p ∨ ¬p,...
               Problem: find set of axioms Ax
Introduction                            Sequences                         Summary and conclusions


Biological sequences
What is a biological sequence?




                                               entity




                   sequence_feature     sequence_operation   sequence_attribute




                junction       region




       What is a sequence, a junction, a sequence operation, etc.?
               axiomatic characterization of basic categories
               axiomatic characterization of basic relations (e.g., mereology)
               integration in top-level/core ontology: sequences as material
               entities, abstract entities, qualities, ...
Introduction                            Sequences                         Summary and conclusions


Biological sequences
What is a biological sequence?



                                               entity




                   sequence_feature     sequence_operation   sequence_attribute




                junction       region




               sequence feature: An extent of biological sequence.
               region: A sequence feature with an extent greater than zero.
               A nucleotide region is composed of bases and a polypeptide
               region is composed of amino acids.
               junction: A sequence feature with an extent of zero.
Introduction                            Sequences                         Summary and conclusions


Biological sequences
What is a biological sequence?



                                               entity




                   sequence_feature     sequence_operation   sequence_attribute




                junction       region




               sequence feature: An extent of biological sequence.
               region: A sequence feature with an extent greater than zero.
               A nucleotide region is composed of bases and a polypeptide
               region is composed of amino acids.
               junction: A sequence feature with an extent of zero.
       But what is a sequence? A region? A junction?
Introduction                        Sequences                     Summary and conclusions


Biological sequences
Chains of molecules




               sequence is a material structure (molecule) with molecules,
               atoms, electrons, etc., as parts
               junction: A binding site between two molecules
               parts of sequence ACAC: ACAC, ACA, CAC, AC, CA, AC, A,
               C, A, C (molecules)
Introduction                         Sequences                      Summary and conclusions


Biological sequences
Syntactic entities specifying chains of molecules




       AGTACGTAGTAGCTGCTGCTACGTGCGCTAGCTAGTACGTCA
       CGACGTAGATGCTAGCTGACTCGATGC
       CGATCGATCGTACGTCGACTGATCGTAGCTACGTCGTACGTAG
       CATCGTCAGTTACTGCATGCTCG
               sequence is a material structure, e.g., patterns of ink, on a
               physical storage medium, in a database
               junction: adjacency between symbols
               parts of sequence ACAC: ACAC, ACA, CAC, AC, CA, AC, A,
               C, A, C
               not all sequences are sequences of molecules
Introduction                        Sequences                      Summary and conclusions


Biological sequences
Abstract information-bearing entities




               sequence is an abstract entity, independent of space and time
               only one sequence ACAC, AC, A, C, etc.
               parts of sequence ACAC: ACAC, ACA, CAC, AC, CA, A, C
               AC and AC (tokens) represent the same sequence
               “sequence motif”
               derived from equivalence class of sequence tokens
Introduction           Sequences   Summary and conclusions


Biological sequences
Overview
Introduction                     Sequences               Summary and conclusions


Biological sequences
Axiom system: mereology




                     sPO(x, y )∧sPO(y , z) → sPO(x, z)              (1)
                          Seq(x) →sPO(x, x)                         (2)
                     sPO(x, y )∧sPO(y , x) → x = y                  (3)
Introduction                     Sequences                    Summary and conclusions


Biological sequences
Axiom system: mereology




                      sPO(x, y )∧sPO(y , z) → sPO(x, z)                  (1)
                          Seq(x) →sPO(x, x)                              (2)
                      sPO(x, y )∧sPO(y , x) → x = y                      (3)



        Seq(x) ∧ Seq(y ) ∧ ¬sPO(x, y ) → ∃z(sPO(z, x) ∧ sdisjoint(z, y ))
                                                                      (4)
Introduction                     Sequences                    Summary and conclusions


Biological sequences
Axiom system: mereology




                      sPO(x, y )∧sPO(y , z) → sPO(x, z)                  (1)
                          Seq(x) →sPO(x, x)                              (2)
                      sPO(x, y )∧sPO(y , x) → x = y                      (3)



        Seq(x) ∧ Seq(y ) ∧ ¬sPO(x, y ) → ∃z(sPO(z, x) ∧ sdisjoint(z, y ))
                                                                      (4)

                      Seq(x) → ∃y (PBS(y ) ∧ sPO(y , x))                 (5)
Introduction                     Sequences                    Summary and conclusions


Biological sequences
Axiom system: mereology




                      sPO(x, y )∧sPO(y , z) → sPO(x, z)                  (1)
                          Seq(x) →sPO(x, x)                              (2)
                      sPO(x, y )∧sPO(y , x) → x = y                      (3)



        Seq(x) ∧ Seq(y ) ∧ ¬sPO(x, y ) → ∃z(sPO(z, x) ∧ sdisjoint(z, y ))
                                                                      (4)

                      Seq(x) → ∃y (PBS(y ) ∧ sPO(y , x))                 (5)

                 Seq(x) →¬∃y (sPPO(y , x) ∧ ∀u(sPPO(u, x)∧
                                                                         (6)
                            PBS(u) → sPO(u, y )))
Introduction                        Sequences   Summary and conclusions


Biological sequences
Axiom system: mereology




       AAACGTTTTTTTTAAAAAAAAA
               sum principle does not hold
               connectedness important
Introduction                         Sequences                    Summary and conclusions


Biological sequences
Axiom system: mereology




       AAACGTTTTTTTTAAAAAAAAA
               sum principle does not hold
               connectedness important
       AAACGTTTTTTTTAAAAAAAAA
               if two sequences overlap, there is a maximal sequence which is
               part of both
               product principle holds
Introduction                          Sequences                      Summary and conclusions


Biological sequences
Axiom system: mereology




               aPO(x, y ) ⇐⇒ ∃a, b(sto(a, y ) ∧ sPO(b, a) ∧ sto(b, x))          (7)


               defined abstract-part-of relation
               based on equivalence classes of tokens
               partial order
               atomar mereology
               no strong supplementation principle!
               no weak supplementation principle!
                    aPPO(x, y ) → ∃z(aPO(z, y ) ∧ adisjoint(z, x))
                    ACACA
Introduction                         Sequences                     Summary and conclusions


Biological sequences
Axiom system




       http://bioonto.de/pmwiki.php/Main/FormalOntologyOfSequences

               foundation axioms available for BFO, DOLCE, GFO
               implementation in the SPASS theorem prover
               need for second-order logic: connectedness, transitive closure
                ∀s∀P(∀x(P(x) ↔ in(x, s)) ∧ ∀Q(∃aQ(a) ∧ ∀x(Q(x) →
                  P(x)) ∧ ∀u, v (Q(u) ∧ conn(u, v ) → Q(v )) →
                  ∀x(P(x) → Q(x))))
Introduction                          Sequences                         Summary and conclusions


Summary and conclusions
Sequence Ontology




               at least three different meanings of “sequence”:
                   chains of molecules (independent continuants)
                   syntactic representations (independent continuants)
                   abstract information-bearing entities (abstract individual,
                   generically dependent continuant)
               the categories have different properties
               high-level distinction in SO
               relations to connect different “sequence” categories
Introduction                         Sequences                     Summary and conclusions


Summary and conclusions
Axioms for relations




               different part-of relations for different domains/applications
               distinguish relations by axioms
               axioms can be added to Relationship Ontology
Introduction                        Sequences                        Summary and conclusions


Summary and conclusions
Strong axiom systems




               ontologies are used in knowledge-based applications
               documentation for humans and machine
               some properties cannot be expressed in OBO/OWL/FOL/...
               development of strong axioms contributes to interoperability
               axioms can help in software development, database design,
               conceptual modelling
Introduction                     Sequences   Summary and conclusions



Acknowledgements




               Heinrich Herre
               Janet Kelso
               three reviewers
Introduction     Sequences   Summary and conclusions




               Thank you!
Introduction                          Sequences         Summary and conclusions


Implementation
Overview




               second-order logics
               quantification over predicates
               formalize minimality and closures
               satisfiability is undecidable
               no sound and complete deductive system
Introduction                Sequences              Summary and conclusions


Implementation
FOL fragment in SPASS




       predicates[(Seq,1),(Mol,1),(ASeq,1),(sPO,2),(PO,2),(aPO,2),
       (binds,2),(Rep,2),(sto,2),(mto,2),
       (sPPO,2),(PBS,1),(soverlap,2),(sdisjoint,2),
       (between,4),(end,3),(in,2),(Jun,1),(conn,2),(conn2,2),
       (PPO,2),(At,1),(overlap,2),(disjoint,2),
       (CSeq,1),(LSeq,1),(DSeq,1),(DMol,1),(equivalent,2),
       (first,3),(last,3)].
Introduction                Sequences              Summary and conclusions


Implementation
FOL fragment in SPASS




       formula(forall([X],implies(Seq(X),sPO(X,X)))).
       formula(forall([X,Y],implies(and(sPO(X,Y),sPO(Y,X)),
       equal(X,Y)))).
       formula(forall([X,Y,Z],implies(and(sPO(X,Y),
       sPO(Y,Z)),sPO(X,Z)))).
       formula(forall([X],implies(Seq(X),exists([Y],
       and(PBS(Y),sPO(Y,X)))))).

Más contenido relacionado

Similar a A formal ontology of sequences

Weakly proregular sequence and Cech, local cohomology
Weakly proregular sequence and Cech, local cohomologyWeakly proregular sequence and Cech, local cohomology
Weakly proregular sequence and Cech, local cohomologyryoya9826
 
JAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning rules
JAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning rulesJAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning rules
JAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning ruleshirokazutanaka
 
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...Gota Morota
 
Skeleton extraction 2011
Skeleton extraction 2011Skeleton extraction 2011
Skeleton extraction 2011apinzonf
 
#8 formal methods – pro logic
#8 formal methods – pro logic#8 formal methods – pro logic
#8 formal methods – pro logicSharif Omar Salem
 
What can your library do for you?
What can your library do for you?What can your library do for you?
What can your library do for you?Rajarshi Guha
 
Functional specialization in human cognition: a large-scale neuroimaging init...
Functional specialization in human cognition: a large-scale neuroimaging init...Functional specialization in human cognition: a large-scale neuroimaging init...
Functional specialization in human cognition: a large-scale neuroimaging init...Ana Luísa Pinho
 
Emerging Approach to Computing Techniques.pptx
Emerging Approach to Computing Techniques.pptxEmerging Approach to Computing Techniques.pptx
Emerging Approach to Computing Techniques.pptxPoonamKumarSharma
 
EVOLUTION OF ONTOLOGY-BASED MAPPINGS
EVOLUTION OF ONTOLOGY-BASED MAPPINGSEVOLUTION OF ONTOLOGY-BASED MAPPINGS
EVOLUTION OF ONTOLOGY-BASED MAPPINGSAksw Group
 
Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...
Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...
Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...Cristiano Longo
 
Protein Structure, Databases and Structural Alignment
Protein Structure, Databases and Structural AlignmentProtein Structure, Databases and Structural Alignment
Protein Structure, Databases and Structural AlignmentSaramita De Chakravarti
 

Similar a A formal ontology of sequences (20)

HPWFcorePRES--FUR2016
HPWFcorePRES--FUR2016HPWFcorePRES--FUR2016
HPWFcorePRES--FUR2016
 
Weakly proregular sequence and Cech, local cohomology
Weakly proregular sequence and Cech, local cohomologyWeakly proregular sequence and Cech, local cohomology
Weakly proregular sequence and Cech, local cohomology
 
JAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning rules
JAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning rulesJAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning rules
JAISTサマースクール2016「脳を知るための理論」講義02 Synaptic Learning rules
 
02 RULES OF INFERENCES.pptx
02 RULES OF INFERENCES.pptx02 RULES OF INFERENCES.pptx
02 RULES OF INFERENCES.pptx
 
Hmm and neural networks
Hmm and neural networksHmm and neural networks
Hmm and neural networks
 
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
 
Skeleton extraction 2011
Skeleton extraction 2011Skeleton extraction 2011
Skeleton extraction 2011
 
#8 formal methods – pro logic
#8 formal methods – pro logic#8 formal methods – pro logic
#8 formal methods – pro logic
 
What can your library do for you?
What can your library do for you?What can your library do for you?
What can your library do for you?
 
Functional specialization in human cognition: a large-scale neuroimaging init...
Functional specialization in human cognition: a large-scale neuroimaging init...Functional specialization in human cognition: a large-scale neuroimaging init...
Functional specialization in human cognition: a large-scale neuroimaging init...
 
UCSD NANO106 - 01 - Introduction to Crystallography
UCSD NANO106 - 01 - Introduction to CrystallographyUCSD NANO106 - 01 - Introduction to Crystallography
UCSD NANO106 - 01 - Introduction to Crystallography
 
Emerging Approach to Computing Techniques.pptx
Emerging Approach to Computing Techniques.pptxEmerging Approach to Computing Techniques.pptx
Emerging Approach to Computing Techniques.pptx
 
EVOLUTION OF ONTOLOGY-BASED MAPPINGS
EVOLUTION OF ONTOLOGY-BASED MAPPINGSEVOLUTION OF ONTOLOGY-BASED MAPPINGS
EVOLUTION OF ONTOLOGY-BASED MAPPINGS
 
Lesson 26
Lesson 26Lesson 26
Lesson 26
 
AI Lesson 26
AI Lesson 26AI Lesson 26
AI Lesson 26
 
Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...
Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...
Herbrand-satisfiability of a Quantified Set-theoretical Fragment (Cantone, Lo...
 
Protein Structure, Databases and Structural Alignment
Protein Structure, Databases and Structural AlignmentProtein Structure, Databases and Structural Alignment
Protein Structure, Databases and Structural Alignment
 
bioexpo_poster
bioexpo_posterbioexpo_poster
bioexpo_poster
 
Statistical Physics Studies of Machine Learning Problems by Lenka Zdeborova, ...
Statistical Physics Studies of Machine Learning Problems by Lenka Zdeborova, ...Statistical Physics Studies of Machine Learning Problems by Lenka Zdeborova, ...
Statistical Physics Studies of Machine Learning Problems by Lenka Zdeborova, ...
 
Reasoning with RNA
Reasoning with RNAReasoning with RNA
Reasoning with RNA
 

Más de Robert Hoehndorf

Ontology-based data access and semantic mining with Aber-OWL
Ontology-based data access and semantic mining with Aber-OWLOntology-based data access and semantic mining with Aber-OWL
Ontology-based data access and semantic mining with Aber-OWLRobert Hoehndorf
 
Integrative and translational analysis of the phenome
Integrative and translational analysis of the phenomeIntegrative and translational analysis of the phenome
Integrative and translational analysis of the phenomeRobert Hoehndorf
 
A translational medicine approach to orphan diseases
A translational medicine approach to orphan diseasesA translational medicine approach to orphan diseases
A translational medicine approach to orphan diseasesRobert Hoehndorf
 
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...Integration of knowledge for personalized medicine: a pharmacogenomics case-s...
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...Robert Hoehndorf
 
Relating functions and dispositions using nonmonotonic reasoning
Relating functions and dispositions using nonmonotonic reasoningRelating functions and dispositions using nonmonotonic reasoning
Relating functions and dispositions using nonmonotonic reasoningRobert Hoehndorf
 
Towards integration of systems biology and biomedical ontologies
Towards integration of systems biology and biomedical ontologiesTowards integration of systems biology and biomedical ontologies
Towards integration of systems biology and biomedical ontologiesRobert Hoehndorf
 
Ontologies for representing, integrating and analyzing phenotypes
Ontologies for representing, integrating and analyzing phenotypesOntologies for representing, integrating and analyzing phenotypes
Ontologies for representing, integrating and analyzing phenotypesRobert Hoehndorf
 

Más de Robert Hoehndorf (8)

Ontology-based data access and semantic mining with Aber-OWL
Ontology-based data access and semantic mining with Aber-OWLOntology-based data access and semantic mining with Aber-OWL
Ontology-based data access and semantic mining with Aber-OWL
 
Flora Phenotype Ontology
Flora Phenotype OntologyFlora Phenotype Ontology
Flora Phenotype Ontology
 
Integrative and translational analysis of the phenome
Integrative and translational analysis of the phenomeIntegrative and translational analysis of the phenome
Integrative and translational analysis of the phenome
 
A translational medicine approach to orphan diseases
A translational medicine approach to orphan diseasesA translational medicine approach to orphan diseases
A translational medicine approach to orphan diseases
 
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...Integration of knowledge for personalized medicine: a pharmacogenomics case-s...
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...
 
Relating functions and dispositions using nonmonotonic reasoning
Relating functions and dispositions using nonmonotonic reasoningRelating functions and dispositions using nonmonotonic reasoning
Relating functions and dispositions using nonmonotonic reasoning
 
Towards integration of systems biology and biomedical ontologies
Towards integration of systems biology and biomedical ontologiesTowards integration of systems biology and biomedical ontologies
Towards integration of systems biology and biomedical ontologies
 
Ontologies for representing, integrating and analyzing phenotypes
Ontologies for representing, integrating and analyzing phenotypesOntologies for representing, integrating and analyzing phenotypes
Ontologies for representing, integrating and analyzing phenotypes
 

Último

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdflior mazor
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Evaluating the top large language models.pdf
Evaluating the top large language models.pdfEvaluating the top large language models.pdf
Evaluating the top large language models.pdfChristopherTHyatt
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfhans926745
 

Último (20)

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Evaluating the top large language models.pdf
Evaluating the top large language models.pdfEvaluating the top large language models.pdf
Evaluating the top large language models.pdf
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 

A formal ontology of sequences

  • 1. Introduction Sequences Summary and conclusions A formal ontology of sequences Robert Hoehndorf1,2 , Janet Kelso2 , Heinrich Herre1 1 Institute for Informatics and Research Group Ontologies in Medicine, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig 2 Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Anthropology
  • 2. Introduction Sequences Summary and conclusions Ontology ontology (philosophy) studies the nature of existence and categories of being an ontology (computer science) is the “explicit specification of a conceptualization of a domain” [Gruber, 1993] specifies the meaning of terms in a vocabulary
  • 3. Introduction Sequences Summary and conclusions Ontology Explicit definitions P(x1 , ..., xn ) ⇐⇒ φ(x1 , ..., xn ) φ does not contain P (non-circular definition), but predicates P1 , ..., Pm . every occurrence of P(x1 , ..., xn ) can be replaced with φ(x1 , ..., xn ) φ(x1 , ..., xn ) contains undefined terms P1 , ..., Pm . define P1 , ..., Pm using terms Q1 , ..., Qr , etc. set of primitive (undefined) terms remaining every statement can be expressed using only primitive terms
  • 4. Introduction Sequences Summary and conclusions Ontology Axiomatic method construct statements using only primitive terms select set of statements T as true within a domain select set of axioms Ax ⊆ T deductively infer true statements from the axioms logical axioms always hold: ¬¬p → p, p ∨ ¬p,... Problem: find set of axioms Ax
  • 5. Introduction Sequences Summary and conclusions Biological sequences What is a biological sequence? entity sequence_feature sequence_operation sequence_attribute junction region What is a sequence, a junction, a sequence operation, etc.? axiomatic characterization of basic categories axiomatic characterization of basic relations (e.g., mereology) integration in top-level/core ontology: sequences as material entities, abstract entities, qualities, ...
  • 6. Introduction Sequences Summary and conclusions Biological sequences What is a biological sequence? entity sequence_feature sequence_operation sequence_attribute junction region sequence feature: An extent of biological sequence. region: A sequence feature with an extent greater than zero. A nucleotide region is composed of bases and a polypeptide region is composed of amino acids. junction: A sequence feature with an extent of zero.
  • 7. Introduction Sequences Summary and conclusions Biological sequences What is a biological sequence? entity sequence_feature sequence_operation sequence_attribute junction region sequence feature: An extent of biological sequence. region: A sequence feature with an extent greater than zero. A nucleotide region is composed of bases and a polypeptide region is composed of amino acids. junction: A sequence feature with an extent of zero. But what is a sequence? A region? A junction?
  • 8. Introduction Sequences Summary and conclusions Biological sequences Chains of molecules sequence is a material structure (molecule) with molecules, atoms, electrons, etc., as parts junction: A binding site between two molecules parts of sequence ACAC: ACAC, ACA, CAC, AC, CA, AC, A, C, A, C (molecules)
  • 9. Introduction Sequences Summary and conclusions Biological sequences Syntactic entities specifying chains of molecules AGTACGTAGTAGCTGCTGCTACGTGCGCTAGCTAGTACGTCA CGACGTAGATGCTAGCTGACTCGATGC CGATCGATCGTACGTCGACTGATCGTAGCTACGTCGTACGTAG CATCGTCAGTTACTGCATGCTCG sequence is a material structure, e.g., patterns of ink, on a physical storage medium, in a database junction: adjacency between symbols parts of sequence ACAC: ACAC, ACA, CAC, AC, CA, AC, A, C, A, C not all sequences are sequences of molecules
  • 10. Introduction Sequences Summary and conclusions Biological sequences Abstract information-bearing entities sequence is an abstract entity, independent of space and time only one sequence ACAC, AC, A, C, etc. parts of sequence ACAC: ACAC, ACA, CAC, AC, CA, A, C AC and AC (tokens) represent the same sequence “sequence motif” derived from equivalence class of sequence tokens
  • 11. Introduction Sequences Summary and conclusions Biological sequences Overview
  • 12. Introduction Sequences Summary and conclusions Biological sequences Axiom system: mereology sPO(x, y )∧sPO(y , z) → sPO(x, z) (1) Seq(x) →sPO(x, x) (2) sPO(x, y )∧sPO(y , x) → x = y (3)
  • 13. Introduction Sequences Summary and conclusions Biological sequences Axiom system: mereology sPO(x, y )∧sPO(y , z) → sPO(x, z) (1) Seq(x) →sPO(x, x) (2) sPO(x, y )∧sPO(y , x) → x = y (3) Seq(x) ∧ Seq(y ) ∧ ¬sPO(x, y ) → ∃z(sPO(z, x) ∧ sdisjoint(z, y )) (4)
  • 14. Introduction Sequences Summary and conclusions Biological sequences Axiom system: mereology sPO(x, y )∧sPO(y , z) → sPO(x, z) (1) Seq(x) →sPO(x, x) (2) sPO(x, y )∧sPO(y , x) → x = y (3) Seq(x) ∧ Seq(y ) ∧ ¬sPO(x, y ) → ∃z(sPO(z, x) ∧ sdisjoint(z, y )) (4) Seq(x) → ∃y (PBS(y ) ∧ sPO(y , x)) (5)
  • 15. Introduction Sequences Summary and conclusions Biological sequences Axiom system: mereology sPO(x, y )∧sPO(y , z) → sPO(x, z) (1) Seq(x) →sPO(x, x) (2) sPO(x, y )∧sPO(y , x) → x = y (3) Seq(x) ∧ Seq(y ) ∧ ¬sPO(x, y ) → ∃z(sPO(z, x) ∧ sdisjoint(z, y )) (4) Seq(x) → ∃y (PBS(y ) ∧ sPO(y , x)) (5) Seq(x) →¬∃y (sPPO(y , x) ∧ ∀u(sPPO(u, x)∧ (6) PBS(u) → sPO(u, y )))
  • 16. Introduction Sequences Summary and conclusions Biological sequences Axiom system: mereology AAACGTTTTTTTTAAAAAAAAA sum principle does not hold connectedness important
  • 17. Introduction Sequences Summary and conclusions Biological sequences Axiom system: mereology AAACGTTTTTTTTAAAAAAAAA sum principle does not hold connectedness important AAACGTTTTTTTTAAAAAAAAA if two sequences overlap, there is a maximal sequence which is part of both product principle holds
  • 18. Introduction Sequences Summary and conclusions Biological sequences Axiom system: mereology aPO(x, y ) ⇐⇒ ∃a, b(sto(a, y ) ∧ sPO(b, a) ∧ sto(b, x)) (7) defined abstract-part-of relation based on equivalence classes of tokens partial order atomar mereology no strong supplementation principle! no weak supplementation principle! aPPO(x, y ) → ∃z(aPO(z, y ) ∧ adisjoint(z, x)) ACACA
  • 19. Introduction Sequences Summary and conclusions Biological sequences Axiom system http://bioonto.de/pmwiki.php/Main/FormalOntologyOfSequences foundation axioms available for BFO, DOLCE, GFO implementation in the SPASS theorem prover need for second-order logic: connectedness, transitive closure ∀s∀P(∀x(P(x) ↔ in(x, s)) ∧ ∀Q(∃aQ(a) ∧ ∀x(Q(x) → P(x)) ∧ ∀u, v (Q(u) ∧ conn(u, v ) → Q(v )) → ∀x(P(x) → Q(x))))
  • 20. Introduction Sequences Summary and conclusions Summary and conclusions Sequence Ontology at least three different meanings of “sequence”: chains of molecules (independent continuants) syntactic representations (independent continuants) abstract information-bearing entities (abstract individual, generically dependent continuant) the categories have different properties high-level distinction in SO relations to connect different “sequence” categories
  • 21. Introduction Sequences Summary and conclusions Summary and conclusions Axioms for relations different part-of relations for different domains/applications distinguish relations by axioms axioms can be added to Relationship Ontology
  • 22. Introduction Sequences Summary and conclusions Summary and conclusions Strong axiom systems ontologies are used in knowledge-based applications documentation for humans and machine some properties cannot be expressed in OBO/OWL/FOL/... development of strong axioms contributes to interoperability axioms can help in software development, database design, conceptual modelling
  • 23. Introduction Sequences Summary and conclusions Acknowledgements Heinrich Herre Janet Kelso three reviewers
  • 24. Introduction Sequences Summary and conclusions Thank you!
  • 25. Introduction Sequences Summary and conclusions Implementation Overview second-order logics quantification over predicates formalize minimality and closures satisfiability is undecidable no sound and complete deductive system
  • 26. Introduction Sequences Summary and conclusions Implementation FOL fragment in SPASS predicates[(Seq,1),(Mol,1),(ASeq,1),(sPO,2),(PO,2),(aPO,2), (binds,2),(Rep,2),(sto,2),(mto,2), (sPPO,2),(PBS,1),(soverlap,2),(sdisjoint,2), (between,4),(end,3),(in,2),(Jun,1),(conn,2),(conn2,2), (PPO,2),(At,1),(overlap,2),(disjoint,2), (CSeq,1),(LSeq,1),(DSeq,1),(DMol,1),(equivalent,2), (first,3),(last,3)].
  • 27. Introduction Sequences Summary and conclusions Implementation FOL fragment in SPASS formula(forall([X],implies(Seq(X),sPO(X,X)))). formula(forall([X,Y],implies(and(sPO(X,Y),sPO(Y,X)), equal(X,Y)))). formula(forall([X,Y,Z],implies(and(sPO(X,Y), sPO(Y,Z)),sPO(X,Z)))). formula(forall([X],implies(Seq(X),exists([Y], and(PBS(Y),sPO(Y,X)))))).