Representing the ChEBI ontology in OWL

•

2 likes•1,163 views

Janna Hastings

Presented at the EBI Industry Programme Ontology tutorial.

Technology

The ChEBI ontology and OWL Industry Programme Ontology Tutorial European Bioinformatics Institute 13 October 2010 Janna Hastings EBI Chemoinformatics and Metabolism

Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

Introduction to ChEBI ,[object Object],[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

ChEBI ontology 4/26/2010 A ChEBI entry page

ChEBI ontology ChEBI ontology 20.10.10 ,[object Object],[object Object],[object Object],[object Object],( R ) -adrenaline

ChEBI ontology 20.10.10 Molecular structure ontology

ChEBI internal ontology architecture ChEBI ontology 20.10.10 Oracle database

A ChEBI entity in OBO format ,[object Object],ChEBI ontology 20.10.10

Expressing ChEBI in OWL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

ChEBI in OWL: Relationship patterns ,[object Object],[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

ChEBI in OWL: Metadata ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

A ChEBI entity in OWL format ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

ChEBI in OWL: Editing and reasoning ,[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

Chemical structures and OWL ,[object Object],ChEBI ontology 20.10.10 caffeine Molecules consist of atoms connected by bonds single bond double bond Carbon atom Hydrogen atom Nitrogen atom Oxygen atom

Structure-based classification ,[object Object],carboxylic acid cyclic molecule if molecule has part some carboxy group if molecule has property cyclic, i.e. a self-connected cyclic path exists through the molecule’s atoms

OWL representation Without internal structure, all parts must be explicitly asserted (which leads to a combinatorial explosion for larger molecules) But the internal structure of complex molecules cannot be encoded in OWL ontology axioms due to their cycles does not have a model in the shape of a tree

Description Graphs ,[object Object],[object Object],[object Object],[object Object]

Rules ,[object Object],[object Object],[object Object],[object Object],[object Object]

Chemical description graphs + Rules defined to detect cycles and parts Reasoning results: able to perform classification (but scalability a concern)

Acknowledgements ,[object Object],[object Object],[object Object],[object Object],ChEBI ontology 20.10.10

Similar to Representing the ChEBI ontology in OWL

Obo and common logic

Chris Mungall

Real World Applications of OWL

Michel Dumontier

Formal representation of models in systems biology

Michel Dumontier

Sw owl rules-proposal

okeee

2011 ebi industry workshop

Michel Dumontier

Towards ubiquitous OWL computing: Simplifying programmatic authoring of and q...

Hilmar Lapp

2009 CSBB LAB 新生訓練

Abner Huang

The Subliminal Toolbox: automating steps in the reconstruction of metabolic n...

Neil Swainston

The Subliminal Toolbox: automating steps in the reconstruction of metabolic n...

Neil Swainston

MIREOT

Melanie Courtot

Experiences in the biosciences with the open biological ontologies foundry an...

Chris Mungall

Macromolecules or Big Small Molecules? Handling biopolymers in a chemical reg...

NextMove Software

Improving the quality of chemical databases with community-developed tools (a...

baoilleach

Biochemical ontologies aim to capture and represent biochemical entities and the relations that exist between them in an accurate manner. A fundamental starting point is biochemical identity, but our current approach for generating identifiers is haphazard and consequently integrating data is error-prone. I will discuss plausible structure-based strategies for biochemical identity whether it be at molecular level or some part thereof (e.g. residues, collection of residues, atoms, collection of atoms, functional groups) such that identifiers may be generated in an automatic and curator/database independent manner. With structure-based identifiers in hand, we will be in a position to more accurately capture context-specific biochemical knowledge, such as how a set of residues in a binding site are involved in a chemical reaction including the fact that a key nitrogen atom must first be de-protonated. Thus, our current representation of biochemical knowledge may improve such that manual and automatic methods of bio-curation are substantially more accurate.

20090511 Manchester Biochemistry

Michel Dumontier

Tutorial OWL and drug discovery ICBO 2013

Samuel Croset

Protein structure classification/domain prediction: SCOP and CATH (Bioinforma...

SELF-EXPLANATORY

All together now: piecing together the knowledge graph of life

Chris Mungall

Systems Biology Model Semantics and Integration

Allyson Lister

Adapt OWL as a Modular Ontology Language

Jie Bao

Ontology-based data access and semantic mining with Aber-OWL

Robert Hoehndorf

Similar to Representing the ChEBI ontology in OWL (20)

Obo and common logic

Real World Applications of OWL

Formal representation of models in systems biology

Sw owl rules-proposal

2011 ebi industry workshop

Towards ubiquitous OWL computing: Simplifying programmatic authoring of and q...

2009 CSBB LAB 新生訓練

The Subliminal Toolbox: automating steps in the reconstruction of metabolic n...

MIREOT

Experiences in the biosciences with the open biological ontologies foundry an...

Macromolecules or Big Small Molecules? Handling biopolymers in a chemical reg...

Improving the quality of chemical databases with community-developed tools (a...

20090511 Manchester Biochemistry

Tutorial OWL and drug discovery ICBO 2013

Protein structure classification/domain prediction: SCOP and CATH (Bioinforma...

All together now: piecing together the knowledge graph of life

Systems Biology Model Semantics and Integration

Adapt OWL as a Modular Ontology Language

Ontology-based data access and semantic mining with Aber-OWL

More from Janna Hastings

Presented at the ACS in Dallas: ChEBI is a database and ontology of chemical entities of biological interest, organised into a structure-based and role-based classification hierarchy. Each entry is extensively annotated with a name, definition and synonyms, other metadata such as cross-references, and chemical structure information where appropriate. In addition to the classification hierarchy, the ontology also contains diverse chemical and ontological relationships. While ChEBI is primarily manually maintained, recent developments have focused on improvements in curation through partial automation of common tasks. We will describe a pipeline we have developed for structure-based classification of chemicals into the ChEBI structural classification. The pipeline connects class-level structural knowledge encoded in Web Ontology Language (OWL) axioms as an extension to the ontology, and structural information specified in standard MOLfiles. We make use of the Chemistry Development Kit, the OWL API and the OWLTools library. Harnessing the pipeline, we are able to suggest the best structural classes for the classification of novel structures within the ChEBI ontology.

Pipeline for automated structure-based classification in the ChEBI ontology

Janna Hastings

Ontology-based Data Integration

Janna Hastings

Using ChEBI to explore the underlying biology in metabolomics studies

Janna Hastings

Chemical classification for the Semantic Web

Janna Hastings

Emotion Ontology and Affective Neuroscience

Janna Hastings

Ontologies for Mental Health and Disease

Janna Hastings

Waves and fields in bio-ontologies

Janna Hastings

Representing addiction in Mental Functioning and Disease ontologies

Janna Hastings

Bio-ontologies in bioinformatics: Growing up challenges

Janna Hastings

Mental functioning ontology for interdisciplinary research into mental diseas...

Janna Hastings

From chemicals to minds: Integrated ontologies in the search for scientific u...

Janna Hastings

Modularity requirements in bio-ontologies: a case study of ChEBI

Janna Hastings

The SHAPES workshop, and Holes in living beings

Janna Hastings

A chemical view into biological systems

Janna Hastings

Chemical diagrams and the IAO

Janna Hastings

The emotion ontology: enabling interdisciplinary research in the affective sc...

Janna Hastings

Hyperontology for the biomedical ontologist

Janna Hastings

Using multiple ontologies to characterise the bioactivity of small molecules

Janna Hastings

Processes and Properties

Janna Hastings

Representing sequences of parts in processes using OWL

Janna Hastings

More from Janna Hastings (20)

Pipeline for automated structure-based classification in the ChEBI ontology

Ontology-based Data Integration

Using ChEBI to explore the underlying biology in metabolomics studies

Chemical classification for the Semantic Web

Emotion Ontology and Affective Neuroscience

Ontologies for Mental Health and Disease

Waves and fields in bio-ontologies

Representing addiction in Mental Functioning and Disease ontologies

Bio-ontologies in bioinformatics: Growing up challenges

Mental functioning ontology for interdisciplinary research into mental diseas...

From chemicals to minds: Integrated ontologies in the search for scientific u...

Modularity requirements in bio-ontologies: a case study of ChEBI

The SHAPES workshop, and Holes in living beings

A chemical view into biological systems

Chemical diagrams and the IAO

The emotion ontology: enabling interdisciplinary research in the affective sc...

Hyperontology for the biomedical ontologist

Using multiple ontologies to characterise the bioactivity of small molecules

Processes and Properties

Representing sequences of parts in processes using OWL

Recently uploaded

AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)

Samir Dash

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Rustici Software

The presentation was made in “Web3 Fusion: Embracing AI and Beyond” is more than a conference; it's a journey into the heart of digital transformation. The conference a provided a platform where the future of technology meets practical application. This three-day hybrid event, set in the heart of innovation, served as a gateway to the latest trends and transformative discussions in AI, Blockchain, IoT, AR/VR, and their collective impact on the information space.

AI in Action: Real World Use Cases by Anitaraj

AnitaRaj43

[BuildWithAI] Introduction to Gemini.pdf

Sandro Moreira

The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Orbitshub

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Zilliz

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

UiPathCommunity

DBX First Quarter 2024 Investor Presentation

Dropbox

Introduction to use of FHIR Documents in ABDM

Kumar Satyam

Passkeys: Developing APIs to enable passwordless authentication Cody Salas, Sr Developer Advocate | Solutions Architect - Yubico Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

apidays

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

DianaGray10

Architecting Cloud Native Applications

WSO2

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

JohnPollard-hybrid-app-RailsConf2024.pptx

JohnPollard37

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc

Recently uploaded (20)

AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Corporate and higher education May webinar.pptx

AI in Action: Real World Use Cases by Anitaraj

[BuildWithAI] Introduction to Gemini.pdf

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Introduction to Multilingual Retrieval Augmented Generation (RAG)

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DBX First Quarter 2024 Investor Presentation

Introduction to use of FHIR Documents in ABDM

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Architecting Cloud Native Applications

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

JohnPollard-hybrid-app-RailsConf2024.pptx

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

Representing the ChEBI ontology in OWL

1. The ChEBI ontology and OWL Industry Programme Ontology Tutorial European Bioinformatics Institute 13 October 2010 Janna Hastings EBI Chemoinformatics and Metabolism

4. ChEBI ontology 4/26/2010 A ChEBI entry page

6. ChEBI ontology 20.10.10 Molecular structure ontology

7. ChEBI ontology 20.10.10 Role ontology

8. ChEBI internal ontology architecture ChEBI ontology 20.10.10 Oracle database

10.

11.

12.

13.

14.

15.

16.

17. OWL representation Without internal structure, all parts must be explicitly asserted (which leads to a combinatorial explosion for larger molecules) But the internal structure of complex molecules cannot be encoded in OWL ontology axioms due to their cycles does not have a model in the shape of a tree

18.

19.

20. Chemical description graphs + Rules defined to detect cycles and parts Reasoning results: able to perform classification (but scalability a concern)

21.

22. Thank you for your attention

Representing the ChEBI ontology in OWL

Recommended

Recommended

More Related Content

Similar to Representing the ChEBI ontology in OWL

Similar to Representing the ChEBI ontology in OWL (20)

More from Janna Hastings

More from Janna Hastings (20)

Recently uploaded

Recently uploaded (20)

Representing the ChEBI ontology in OWL