An ontology-based framework for bioinformatics workflows

Digiampietri, Luciano Antônio; Pérez-Alcázar, José de Jesús; Medeiros, Cláudia Bauzer

doi:10.1504/ijbra.2007.015003

Cited by 4 publications

(19 citation statements)

References 22 publications

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“…Then, the ranking score could be used to avoid checking too many patterns [46]. To the best of our knowledge existing solutions for semantic-based workflow mining [11,15,17,18] propose domain-specific solutions while producing substantially different types of abstract models. Therefore, comparing our framework with systems like Proteus or eSysbio in terms of generated models, while conceivable, is not a trivial task.…”

Section: Discussionmentioning

confidence: 99%

“…Formulating queries could be a very difficult task if users don't know what is the best category for the next step. Digiampietri et al [15] propose the SHOP2 specification which comprises three sections: a domain [2] www.ebi.ac.uk/Tools/emboss [3] www.esysbio.org definition (defdomain), a problem definition (defproblem) and a problem resolver (find-plans) where the latter is being in charge of finding the plans to solve the targeted problem. The respective SHOP2 planner supports generalization/specialization hierarchies of operators (tasks) and handles complex objects: structures created from basic objects by composition.…”

Section: Introductionmentioning

confidence: 99%

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Towards an ontology-based recommender system for relevant bioinformatics workflows

Halioui

Valtchev

Diallo

2016

Preprint

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Background: With the large and diverse type of biological data, bioinformatic solutions are being more complex and computationally intensive. New specialized data skills need to be acquired by researchers in order to follow this development. Workflow Management Systems rise as an efficient way to automate tasks through abstract models in order to assist users during their problem solving tasks. However, current solutions could have several problems in reusing the developed models for given tasks. The large amount of heterogenous data and the lack of knowledge in using bioinformatics tools could mislead the users during their analyses. To tackle this issue, we propose an ontology-based workflow-mining framework generating semantic models of bioinformatic best practices in order to assist scientists. To this end, concrete workflows are extracted from scientific articles and then mined using a rich domain ontology.Results: In this study, we explore the specific topics of phylogenetic analyses. We annotated more than 300 recent articles using different ontological concepts and relations. Relative supports (frequencies) of discovered workflow components in texts show interesting results of relevant resources currently used in the different phylogenetic analysis steps. Mining concrete workflows from texts lead us to discover abstract but relevant patterns of the best combinations of tools, parameters and input data for specific phylogenetic problems.Conclusions: Extracted patterns would make workflows more intuitive and easy to be reused in similar situations. This could provide a stepping-stone into the identification of best practices and pave the road to a recommender system.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards an ontology-based recommender system for relevant bioinformatics workflows

Halioui

Valtchev

Diallo

2016

Preprint

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“…However, ontologies cannot include semantics of tests and effectively address dynamic changes in genome testing routines. Several successful efforts have been documented with automatic generation of scientific workflows through technical planning based on ontology descriptions [5][6][7]. As a downside, these approaches do not support the control of long transactions.…”

Section: Related Workmentioning

confidence: 99%

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

et al. 2011

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BackgroundRecent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes.ResultsThis paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra.ConclusionsThis paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.

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“…Using the BPMN framework, XPDL and Wf‐XML are now widely accepted standards that are applied in dozens of software packages such as SemTalk, and are used for the formal specification of workflows in neighboring disciplines such as bioinformatics (Digiampietri et al 2007). Formally, each of these can be proven using Petri Nets (van der Aalst and Ter Hofstede 2000), which are increasingly being supported/implemented in process modeling software packages.…”

Section: State Of the Art: In Theorymentioning

confidence: 99%

Geo‐ontology Tools: The Missing Link

Albrecht

Derman

Ramasubramanian

2008

Transactions in GIS

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Numerous authors have presented ontology building tools that have all been developed as part of academic projects and that are usually adaptations of more generic tools for geo‐spatial applications. While we trust that these tools do their job for the special purpose they have been built, the GIScience user community is still a long way away from off‐the‐shelf ontology builders that can be used by GIS project managers. In this article, we present a comparative study of ontology building tools described in some twenty peer‐reviewed GIScience journal articles. We analyze them from the perspective of two application domains, crime analysis and transportation/land use. For the latter, we developed a database schema, which is substantially different from the three main templates commonly used. The crime analysis application uses a rule base for an agent‐based model that had no precursor. In both cases, the currently available set of tools cannot replace manual coding of ontologies for use with ESRI‐based application software. Based on these experiences, we outline a requirements list of what the tools described in the first part of the article are missing to make them practical from an applications perspective. The result is an R&D agenda for this important aspect of GIScience.

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An ontology-based framework for bioinformatics workflows

Cited by 4 publications

References 22 publications

Towards an ontology-based recommender system for relevant bioinformatics workflows

Towards an ontology-based recommender system for relevant bioinformatics workflows

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

Geo‐ontology Tools: The Missing Link

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