Ontological dependence, dispositions and institutional reality in chemistryJanna Hastings
Presented at the 2010 Formal Ontology in Information Systems conference (FOIS 2010). Discusses different classifications of the activity of chemical entities (in the context of the ChEBI ontology).
Presented at the 2nd ChEBI User Group Workshop. Discusses some of the difficulties encountered in the project which aims to classify chemicals in the ChEBI ontology automatically based on their structures.
Pipeline for automated structure-based classification in the ChEBI ontologyJanna 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.
The emotion ontology: enabling interdisciplinary research in the affective sc...Janna Hastings
Presented at the 2011 ICBO, we motivate and introduce the Emotion Ontology currently under development in the Swiss Centre for Affective Sciences in collaboration with the University at Buffalo.
Using ChEBI to explore the underlying biology in metabolomics studiesJanna Hastings
ChEBI is a chemical database and ontology that is widely used to annotate biological data. Here, we show a tool that is currently in development that allows exploration of the biological annotations for metabolites that are found to be enriched in metabolomics investigations. This tool will be made available online soon.
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
Ontological dependence, dispositions and institutional reality in chemistryJanna Hastings
Presented at the 2010 Formal Ontology in Information Systems conference (FOIS 2010). Discusses different classifications of the activity of chemical entities (in the context of the ChEBI ontology).
Presented at the 2nd ChEBI User Group Workshop. Discusses some of the difficulties encountered in the project which aims to classify chemicals in the ChEBI ontology automatically based on their structures.
Pipeline for automated structure-based classification in the ChEBI ontologyJanna 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.
The emotion ontology: enabling interdisciplinary research in the affective sc...Janna Hastings
Presented at the 2011 ICBO, we motivate and introduce the Emotion Ontology currently under development in the Swiss Centre for Affective Sciences in collaboration with the University at Buffalo.
Using ChEBI to explore the underlying biology in metabolomics studiesJanna Hastings
ChEBI is a chemical database and ontology that is widely used to annotate biological data. Here, we show a tool that is currently in development that allows exploration of the biological annotations for metabolites that are found to be enriched in metabolomics investigations. This tool will be made available online soon.
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
Hyperontology for the biomedical ontologistJanna Hastings
Presented at the 2011 ICBO Workshop on working with multiple biomedical ontologies. We present a framework for designing and interrelating ontology modules which are indvidually represented in different underlying logical formalisms.
Using multiple ontologies to characterise the bioactivity of small moleculesJanna Hastings
Presented at the 2011 ICBO workshop on working with multiple biomedical ontologies. We describe work on text mining for relationship extraction between chemical and biological entities via a language model for bioactivity.
The Translational Medicine Ontology: Driving personalized medicine by br...Michel Dumontier
The Translational Medicine Ontology provides terminology that bridges diverse areas of translational medicine including hypothesis management, discovery research, drug development and formulation, clinical research, and clinical practice. Designed primarily from use cases, the ontology consists of essential terms that are mapped to other ontologies. It serves as a global schema for data integration while simultaneously facilitating the formulation of complex queries across heterogeneous sources. We demonstrate the utility of the ontology through question answering over a prototype knowledge base composed of sample patient data integrated with linked open data. This work forms a basis for the development of a computational platform for managing information relevant to personalized medicine.
Atsdr summary of the evidence for presumption draft for va 9.21.15Lori Freshwater
ATSDR Assessment of the Evidence for the Drinking Water
Contaminants at Camp Lejeune and Specific Cancers and Other
Diseases. September 11, 2015. (DRAFT)
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
Here is a copy of the presentation that I gave to MRC CBU at Cambridge University on the 5th July 2017, essentially a summary of a book chapter of mine to be published later this year. The focus of my presentation was on connections between #self, #other and our #connections with the environment.
A keynote presentation at the NBIC Annual Meeting. Covers the concept of polypharmacology, a bioinformatics approach to off-target binding and a systems approach to dynamical modeling of the process.
Rose S, Frye RE, Slattery J, Wynne R, Tippett M, et al. (2014) Oxidative Stress Induces Mitochondrial Dysfunction in a Subset of Autism Lymphoblastoid Cell Lines in a Well-Matched Case Control Cohort. PLoS ONE 9(1):e85436.doi:10.1371/journal.pone.0085436.
Evidence-Based Treatments of AddictionAuthor(s) Charles P. .docxgitagrimston
Evidence-Based Treatments of Addiction
Author(s): Charles P. O'Brien
Source: Philosophical Transactions: Biological Sciences, Vol. 363, No. 1507, The Neurobiology of
Addiction: New Vistas (Oct. 12, 2008), pp. 3277-3286
Published by: The Royal Society
Stable URL: http://www.jstor.org/stable/20208741 .
Accessed: 05/12/2014 15:41
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact [email protected]
.
The Royal Society is collaborating with JSTOR to digitize, preserve and extend access to Philosophical
Transactions: Biological Sciences.
http://www.jstor.org
This content downloaded from 206.224.223.240 on Fri, 5 Dec 2014 15:41:30 PM
All use subject to JSTOR Terms and Conditions
http://www.jstor.org/action/showPublisher?publisherCode=rsl
http://www.jstor.org/stable/20208741?origin=JSTOR-pdf
http://www.jstor.org/page/info/about/policies/terms.jsp
http://www.jstor.org/page/info/about/policies/terms.jsp
PHILOSOPHICAL
TRANSACTIONS
_of-?TT^
PhiL Trans' R' Soc' B (2008) 363' 3277~3286
THE ROYAL 4\ doi:10.1098/rstb.2008.0105
SOCIETY JAJJ Published online 18 July 2008
Review
Evidence-based treatments of addiction
Charles P. O'Brien*
Department of Psychiatry, University of Pennsylvania, 3900 Chestnut Street,
Philadelphia, PA 19104-6178, USA
Both pharmacotherapy and behavioural treatment are required to relieve the symptoms of addictive
disorders. This paper reviews the evidence for the benefits of pharmacotherapy and discusses
mechanisms where possible. Animal models of addiction have led to some medications that are effective
in reducing symptoms and improving function but they do not produce a cure. Addiction is a chronic
disease that tends to recur when treatment is stopped; thus, long-term treatment is recommended.
Keywords: addiction; relapse; withdrawal; endophenotype
1. INTRODUCTION
Most theories of drug-addiction mechanisms have been
based on animal models and, until recently, these
theories have made the assumption that all subjects are
alike in their responses to drugs (Deroche-Gamonet
et ah 2004). In reality, human subjects are quite
variable in how they respond to drugs. Moreover,
data from the studies of non-human primates indicate
that genetic variation is also important in other higher
species. Drugs that demonstrate rewarding properties
in animals also tend to be abused by humans, but only
by a relatively small percentage of those humans
exposed (table 1). The most obvious effects of chronic
drug use are tolerance and physiological dependence
and these phenomena trans ...
Chemical classification for the Semantic WebJanna Hastings
Classification conveys the type for data that is published in the Semantic Web. Classification using OWL ontoloiges dramatically enhances the potential of the chemical Semantic Web. ChEBI provides a classification that can be used across multiple data resources.
Hyperontology for the biomedical ontologistJanna Hastings
Presented at the 2011 ICBO Workshop on working with multiple biomedical ontologies. We present a framework for designing and interrelating ontology modules which are indvidually represented in different underlying logical formalisms.
Using multiple ontologies to characterise the bioactivity of small moleculesJanna Hastings
Presented at the 2011 ICBO workshop on working with multiple biomedical ontologies. We describe work on text mining for relationship extraction between chemical and biological entities via a language model for bioactivity.
The Translational Medicine Ontology: Driving personalized medicine by br...Michel Dumontier
The Translational Medicine Ontology provides terminology that bridges diverse areas of translational medicine including hypothesis management, discovery research, drug development and formulation, clinical research, and clinical practice. Designed primarily from use cases, the ontology consists of essential terms that are mapped to other ontologies. It serves as a global schema for data integration while simultaneously facilitating the formulation of complex queries across heterogeneous sources. We demonstrate the utility of the ontology through question answering over a prototype knowledge base composed of sample patient data integrated with linked open data. This work forms a basis for the development of a computational platform for managing information relevant to personalized medicine.
Atsdr summary of the evidence for presumption draft for va 9.21.15Lori Freshwater
ATSDR Assessment of the Evidence for the Drinking Water
Contaminants at Camp Lejeune and Specific Cancers and Other
Diseases. September 11, 2015. (DRAFT)
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
Here is a copy of the presentation that I gave to MRC CBU at Cambridge University on the 5th July 2017, essentially a summary of a book chapter of mine to be published later this year. The focus of my presentation was on connections between #self, #other and our #connections with the environment.
A keynote presentation at the NBIC Annual Meeting. Covers the concept of polypharmacology, a bioinformatics approach to off-target binding and a systems approach to dynamical modeling of the process.
Rose S, Frye RE, Slattery J, Wynne R, Tippett M, et al. (2014) Oxidative Stress Induces Mitochondrial Dysfunction in a Subset of Autism Lymphoblastoid Cell Lines in a Well-Matched Case Control Cohort. PLoS ONE 9(1):e85436.doi:10.1371/journal.pone.0085436.
Evidence-Based Treatments of AddictionAuthor(s) Charles P. .docxgitagrimston
Evidence-Based Treatments of Addiction
Author(s): Charles P. O'Brien
Source: Philosophical Transactions: Biological Sciences, Vol. 363, No. 1507, The Neurobiology of
Addiction: New Vistas (Oct. 12, 2008), pp. 3277-3286
Published by: The Royal Society
Stable URL: http://www.jstor.org/stable/20208741 .
Accessed: 05/12/2014 15:41
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact [email protected]
.
The Royal Society is collaborating with JSTOR to digitize, preserve and extend access to Philosophical
Transactions: Biological Sciences.
http://www.jstor.org
This content downloaded from 206.224.223.240 on Fri, 5 Dec 2014 15:41:30 PM
All use subject to JSTOR Terms and Conditions
http://www.jstor.org/action/showPublisher?publisherCode=rsl
http://www.jstor.org/stable/20208741?origin=JSTOR-pdf
http://www.jstor.org/page/info/about/policies/terms.jsp
http://www.jstor.org/page/info/about/policies/terms.jsp
PHILOSOPHICAL
TRANSACTIONS
_of-?TT^
PhiL Trans' R' Soc' B (2008) 363' 3277~3286
THE ROYAL 4\ doi:10.1098/rstb.2008.0105
SOCIETY JAJJ Published online 18 July 2008
Review
Evidence-based treatments of addiction
Charles P. O'Brien*
Department of Psychiatry, University of Pennsylvania, 3900 Chestnut Street,
Philadelphia, PA 19104-6178, USA
Both pharmacotherapy and behavioural treatment are required to relieve the symptoms of addictive
disorders. This paper reviews the evidence for the benefits of pharmacotherapy and discusses
mechanisms where possible. Animal models of addiction have led to some medications that are effective
in reducing symptoms and improving function but they do not produce a cure. Addiction is a chronic
disease that tends to recur when treatment is stopped; thus, long-term treatment is recommended.
Keywords: addiction; relapse; withdrawal; endophenotype
1. INTRODUCTION
Most theories of drug-addiction mechanisms have been
based on animal models and, until recently, these
theories have made the assumption that all subjects are
alike in their responses to drugs (Deroche-Gamonet
et ah 2004). In reality, human subjects are quite
variable in how they respond to drugs. Moreover,
data from the studies of non-human primates indicate
that genetic variation is also important in other higher
species. Drugs that demonstrate rewarding properties
in animals also tend to be abused by humans, but only
by a relatively small percentage of those humans
exposed (table 1). The most obvious effects of chronic
drug use are tolerance and physiological dependence
and these phenomena trans ...
Chemical classification for the Semantic WebJanna Hastings
Classification conveys the type for data that is published in the Semantic Web. Classification using OWL ontoloiges dramatically enhances the potential of the chemical Semantic Web. ChEBI provides a classification that can be used across multiple data resources.
Representing addiction in Mental Functioning and Disease ontologiesJanna Hastings
Enabling querying and browsing of biomedical and neuroscientific research on addiction using interoperable ontologies and cross-products. Presented at ICBO 2012.
Bio-ontologies in bioinformatics: Growing up challengesJanna Hastings
Bio-ontologies are growing up, and their use is becoming widespread in many areas of computational science. The new maturity is bringing new challenges, however, in particular visualization of complex ontologies; moving from OBO to OWL; using multiple ontologies in conjunction; training appropriate for biologists and community building.
Mental functioning ontology for interdisciplinary research into mental diseas...Janna Hastings
Presented at the Neuroscience Information Framework (NIF) webinar series on 24/04/2012. An overview of the Mental Functioning Ontology aims and objectives.
From chemicals to minds: Integrated ontologies in the search for scientific u...Janna Hastings
Presented at the 2012 Interdisciplinary Ontology (InterOntology) Conference in Tokyo, February 24th 2012. This presentation gives a whirlwind tour of some "reports from the front lines" of practical bio-ontology development in ChEBI and in the Mental Functioning and Emotion Ontology projects.
Modularity requirements in bio-ontologies: a case study of ChEBIJanna Hastings
A wish list for tools for modularity support in bio-ontology engineering based on the ChEBI ontology requirements. Presented at the workshop on modular ontologies, WoMO, 2011, in Ljubljana.
The SHAPES workshop, and Holes in living beings Janna Hastings
The SHAPES workshop brought together interdisciplinary shape researchers. Our paper presents some challenges in applying shapes -- and holes -- in living beings.
Presented at the AI center of the Stanford Research Institute: chemical ontologies provide a chemical view into biological systems. Various challenges with modelling "active properties" (roles, functions, dispositions) are discussed.
Presented at the ICBO 2011 conference in Buffalo, we tackle the controversial 'is about' relationship in the information artifact ontology (IAO) in the context of chemical diagrams.
Presented at the 2011 ISMB Bio-ontologies SIG. A detour into the difficulties of representing the properties of processes in ontologies, and some steps towar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™
Modelling metabolite concentrations in OWL using Pronto
1. OWLED 2011 Modelling threshold phenomena in OWL:Metabolite concentrations as evidence for disorders Janna Hastings 1,2 Ludger Jansen 3,4 Christoph Steinbeck 1 Stefan Schulz 5 1Chemoinformatics and Metabolism, European Bioinformatics Institute, UK 2 Swiss Centre for Affective Sciences, University of Geneva, Switzerland 3 Department of Philosophy, University of Rostock, Germany 4 Department of Philosophy, RWTH Aachen University, Germany 5 Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria
2. Motivation How do we link chemical entities to diseases? Chemicals can be used as drugs to treatdiseases But also, chemicals infuse living organisms as metabolites: by-products of metabolic processes that indicate which of those processes have taken place Wednesday, June 08, 2011 2 Metabolite concentrations as evidence for disorders (OWLED 2011)
3. ChEBI ChEBI is an ontology for chemicalswhich appear in a biological context Chemical entities, such as molecules and ions are classified structurally, and assigned to one or more roles Examples: antioxidant, analgesic drug, cyclooxygenase inhibitor, ... metabolite Wednesday, June 08, 2011 3 Metabolite concentrations as evidence for disorders (OWLED 2011)
4. ChEBI Roles Wednesday, June 08, 2011 4 Metabolite concentrations as evidence for disorders (OWLED 2011)
5. Metabolites in ChEBI Wednesday, June 08, 2011 5 Metabolite concentrations as evidence for disorders (OWLED 2011)
6. Contextual information In which organism(s) is the molecule a metabolite? How much (what concentration) of this metabolite is normally present in different bio-fluids of those organisms? Which disorders are associated with abnormal levels (increased or decreased) of this metabolite? Wednesday, June 08, 2011 6 Metabolite concentrations as evidence for disorders (OWLED 2011)
7. Human Metabolome DB Database of humanmetabolites and associated contextual information Includes measured concentration valuesfrom different human samples under different conditions (specified as free text!) Wednesday, June 08, 2011 7 Wishart DS, Knox C, Guo AC, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009 37(Database issue):D603-610. Metabolite concentrations as evidence for disorders (OWLED 2011)
8. Metabolite concentrations and OWL Numeric data (OWL data ranges; DL concrete domains) Link between concentrations and disorders is not certain, but a concentration of some metabolite above a certain threshold isconsidered evidence for the presence of a disorder Threshold between normal and abnormal levels is vague(no definite cut-off) Wednesday, June 08, 2011 8 Metabolite concentrations as evidence for disorders (OWLED 2011)
9. We extract: Metabolite concentration values for metabolites found in ChEBI where both a normal and an abnormal value are present for an adult subject The difference between the normal and abnormal concentration indicates a thresholdbetween these scenarios Wednesday, June 08, 2011 9 Data extraction Metabolite concentrations as evidence for disorders (OWLED 2011)
10. Reasoning with OWL data ranges Can we use the ontology to automatically differentiate normal from abnormal concentrations? Wednesday, June 08, 2011 10 4440 uM (normal adult) 7000 uM (adult with diabetes) D-glucosein blood measured value(abnormal) measured value(normal) threshold metaboliteconcentration abnormal Metabolite concentrations as evidence for disorders (OWLED 2011)
11. Generated ontology Wednesday, June 08, 2011 11 `concentration of D-glucose in Blood associated with Diabetes mellitus type 2' equivalentTo ( `concentration in blood' and (hasMetabolite some `portion of D-glucose') and (hasConcentrationValue some double[>= 5700.0]) ) Metabolite concentrations as evidence for disorders (OWLED 2011)
12. Uncertainty Individual differences mean that we can’t straightforwardly associate an abnormal metabolite concentration with a disorder Rather, we want to infer the likelihood(risk) that a patient has a given disorder, given their metabolite concentration value Wednesday, June 08, 2011 12 ? Metabolite concentrations as evidence for disorders (OWLED 2011)
13. Probabilistic DLs Probabilistic DLs extend traditional DLs with the ability to associate with each axiom in the ontology a probability valuewhich represents the degree of certainty of the axiom. Probabilistic knowledge consists of conditional constraints: (v | j) [l, u] with l, u real numbers in the range [0, 1] encodes that j is a subclass of v with probability between l and u. Wednesday, June 08, 2011 13 Metabolite concentrations as evidence for disorders (OWLED 2011)
14. PRONTO A probabilistic, non-monotonic extension to Pellet Accepts probabilistic axioms of the form X subClassOf Y [l, u] (as annotations: pronto:certainty) Version 0.2 with slight modification: upgraded to the latest Pellet and OWL API releases Klinov, P.: Pronto: A Non-monotonic Probabilistic Description Logic Reasoner. Lecture Notes in Computer Science, vol. 5021, chap. 66, pp. 822-826. Wednesday, June 08, 2011 14 Metabolite concentrations as evidence for disorders (OWLED 2011)
15. Discretization We assume disorder risk varies continuouslywith metabolite concentration However, Pronto accepts only discreteranges Wednesday, June 08, 2011 15 high measured value(normal) measured value(abnormal) threshold probability of associated disorder metaboliteconcentration low mediumrisk low risk high risk Metabolite concentrations as evidence for disorders (OWLED 2011)
16. Reasoning with probabilities Wednesday, June 08, 2011 16 2 what is the likelihood that this person has this disorder? (reasoning based on probabilistic constraints) Low risk 0.00—0.24 Disorder Medium risk concentration in blood 0.25—0.54 High risk 0.55—1.00 1 what risk category is this concentration? (reasoning based on data restrictions) Metabolite concentrations as evidence for disorders (OWLED 2011)
17. Results Wednesday, June 08, 2011 17 … Metabolite concentrations as evidence for disorders (OWLED 2011)
18. Combining different evidence Can we accumulate the evidence (i.e. increase the likelihood) for the presence of a given disorder if there are multiple metabolite concentration values pointing towards it? Wednesday, June 08, 2011 18 concentration of D-glucose in blood Diabetes concentration of Acetoacetic acid in blood BARRY Metabolite concentrations as evidence for disorders (OWLED 2011)
19. Results: no conflict (Union) Pronto will combinethe probabilistic constraints medium risk [0.25; 0.54] and high risk [0.55; 1.00] Barry’s risk of having diabetes is in [0.25; 1.00] Wednesday, June 08, 2011 19 Metabolite concentrations as evidence for disorders (OWLED 2011)
20. Results: conflict (Intersection) What happens if Pronto combines probabilistic constraints that overlap? medium [0.25; 0.55] and high risk [0.55; 1.00] Barry’s risk : [0.55; 0.55] medium [0.25; 0.60] and high risk [0.55; 1.00] Barry’s risk : [0.55; 0.60] Wednesday, June 08, 2011 20 Metabolite concentrations as evidence for disorders (OWLED 2011)
21. Discussion Our intuitive requirement is not met: multiple forms of evidence for the same conclusion strengthen the likelihood of that conclusion To address this, Pronto allows creating overriding constraints in sub-classes Wednesday, June 08, 2011 21 + = Medium risk High risk Medium-high risk e.g. [0.54;0.85] Metabolite concentrations as evidence for disorders (OWLED 2011)
22. Limitations We did not attempt: Combined reasoning with more than two conflicting or non-conflicting constraints; Linking the generated ontology to the rest of ChEBI and to a relevant disease ontology; Applying probabilistic constraints to all possible diseases and metabolites in generated ontology; and Systematic performance evaluation of Pronto for this use case Wednesday, June 08, 2011 22 Metabolite concentrations as evidence for disorders (OWLED 2011)
23. OWL and uncertainty Accurately modelling the association between metabolites and disorders requires a semantics for uncertainty Reasoner behaviour when combining different constraints is crucial for adequate applicability to different use cases Future work will involve evaluating alternative probabilistic DLs based on Bayesian networks Wednesday, June 08, 2011 23 Metabolite concentrations as evidence for disorders (OWLED 2011)
24. Conclusion OWL 2 (with data properties and restrictions) and probabilistic DL (as implemented in Pronto) CAN be used to represent the chemical—disease association via metabolite concentration values The ontology (META.owl) and software (META.zip) are available for download from http://www.ebi.ac.uk/~hastings/concentrations/. Wednesday, June 08, 2011 24 Metabolite concentrations as evidence for disorders (OWLED 2011)
25. Acknowledgements Funding BBSRC, grant agreement number BB/G022747/1 within the "Bioinformatics and biological resources" fund; and DFG, grant agreement number JA 1904/2-1, SCHU 2515/1-1 GoodOD(Good Ontology Design). Wednesday, June 08, 2011 25 Metabolite concentrations as evidence for disorders (OWLED 2011)
Editor's Notes
While genomic and proteomic information describe the overallcellular machinery available to an organism, the metabolic profile ofan individual at a given time provides a canvas as to the current physiologicalstate. Concentration levels of relevant metabolites vary underdifferent conditions, in particular, in the presence or absence of differentdisorders.
780 chemical entities with chemical structures have associated the role ‘metabolite’.This information is somewhat useful for the clustering of molecules – at least it allows us to distinguish those molecules that can be metabolites from those that cannot – but it is far too general. We can do much better.
ChEBI roles represent activeproperties of chemical entities – what chemicals do in biological contextsThis information is enhanced by specific representation of the context in which the chemicals are so activeFor metabolites, contextual information includes: - which organism (taxonomy) - how much of the metabolite is usually (normally) present in different body fluids - which disorders are associated to abnormal levels in different body fluids
The HMDB is a database collecting together knowledge about all known human metabolites, including physicochemical, spectral, clinical, biochemical and genomic informationFor each metabolite, HMDB includes measured concentration values taken from human samples of different biofluids (such as blood, urine, cerebrospinal fluid), from persons of different ages and with different underlying conditions.
In some cases the link between certain concentration values and the associated disorders can be pretty close to certain – consider – pregnancy testing.
HMDB data is parsed from its MetaboCards download formatWe extract metabolite concentrations from HMDB where there are both a normal and an abnormal (associated with some disease) concentration level for an adult subject. The difference between the normal and abnormal concentration values indicates a threshold between these scenariosWe want to infer the likelihood of presence of disorder by virtue of the numeric concentration value being closer to the known disordered concentration than to the known normal concentration.
uM = micromolar (1e-6 M)
Problem with standard OWL inferences and uncertainty...Introduce probabilistic DL...
Lukasiewicz, T.: Probabilistic description logics for the semantic web. TU Vienna infsys research report (2007)
We create classes for the categories of low, medium and high risk of having the given disorder.Note that the variation of risk with concentration value can be thought of, asa simplifying assumption, as a continuously valued function ranging over allpossible concentration values. However, as Pronto constraints take the formof intervals associated with classes (or instances), to create a finite numberof OWL classes and associate probability intervals to them, it is necessary todiscretize the probability function into fixed ranges.
What is the risk that Barry has diabetes?
Pronto’s strategy for combining two constraints, in the absence of a conflict, resembles a data union operationWhen multiple constraints conflict, Pronto prefers more specific statements toless specific. We evaluated this behaviour by changing the medium risk constraintto overlap with the high risk constraint, setting the upper bound for mediumto 0.55 instead of 0.54. In this case, Pronto concludes that the probability forBarry having diabetes is [0.55;0.55] -- the most specific (narrowest) resolution. Ifthe medium risk ranges to 0.6, Pronto entails Barry the range [0.55;0.6]. Thus,it seems that the behaviour on conflict (at least for the two-axiom scenario wetest here) resembles an intersection of the two underlying data ranges.
Neither of these results is an optimal representation of the intuitive requirement driven by the use case: it would be betterif the probabilistic combination of different types of evidence for the same conclusion increased the certainty of the conclusion. However, Pronto does allow for overriding inherited constraints in more specific subclasses. Thus, we can specify a new risk subclass for Barry's combined risk categories, and associate this with the disease with a new probability range (e.g. [0.54;0.85]). However,this approach is in general somewhat cumbersome as it would require adding many more classes and constraints to the knowledge base -- for all interesting combinations of risk factors.