The First RSBI (ISA-TAB) Workshop: “Can a Simple Format Work for Complex Studies?”

Sansone, Susanna-Assunta; Rocca-Serra, Philippe; Brandizi, Marco; Brāzma, Alvis; Field, Dawn; Fostel, Jennifer; Garrow, Andrew G.; Gilbert, Jack A.; Goodsaid, Federico; Hardy, Nigel; Jones, Phil; Lister, Allyson L.; Miller, Michael; Morrison, Norman; Rayner, Tim; Sklyar, Nataliya; Taylor, Chris; Tong, Weida; Warner, Guy C.; Wiemann, Stefan

doi:10.1089/omi.2008.0019

Cited by 81 publications

(76 citation statements)

References 24 publications

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“…So, it uses ISATab handler that allows the linkage of a single sample to multiple analyses employing various assays [21].…”

Section: -Analysis Of All Sets Based On Dnn 3-cluster Weighted Workfmentioning

confidence: 99%

Scientific Workflow Clustering Based on Motif Discovery

Koohi-Var¹,

Zahedi²

2017

IJCSEIT

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In this paper, clustering of scientific workflows is investigated. It proposes a work to encode workflows through workflow representations as sets of embedded workflows. Then, it embeds extracted workflow motifs in sets of workflows. By motifs, common patterns of workflow steps and relationships are replaced with indices. Motifs are defined as small functional units that occur much more frequently than expected. They can show hidden relationships, and they keep as much underlying information as possible. In order to have a good estimate on distances between observed workflows, this work proposes the scientific workflow clustering problem with exploiting set descriptors, instead of vector based descriptors. It uses k-means algorithm as a popular clustering algorithm for workflow clustering. However, one of the biggest limitations of the k-means algorithm is the requirement of the number of clusters, K, to be specified before the algorithm is applied. To address this problem it proposes a method based on the SFLA. The simulation results show that the proposed method is better than PSO and GA algorithms in the K selection.

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“…So, it uses ISATab handler that allows the linkage of a single sample to multiple analyses employing various assays [21].…”

Section: -Analysis Of All Sets Based On Dnn 3-cluster Weighted Workfmentioning

confidence: 99%

Scientific Workflow Clustering Based on Motif Discovery

Koohi-Var¹,

Zahedi²

2017

IJCSEIT

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“…19 Any nanomaterial structure representation files, which are not associated with specific Assay file "Measurement Value […]" entries, should be linked to the corresponding Material file using ZIP archives specified in the appropriate "Material Data File" column entry. 20 Empty "Factor Value […]", "Parameter Value […]" or "Measurement Value […]" columns in Study or Assay files can be deleted without having to update the corresponding Investigation file "Study Protocol Parameters Name", "Study Factor Name", or "Study Assay Measurement Name" fields. 21…”

Section: Business Rule Nomentioning

confidence: 99%

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

Robinson¹,

Cronin²,

Richarz³

et al. 2016

Nano Online

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Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed.These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.

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“…In order to facilitate the organization of highly complex imaging research data in a form that balances flexibility with the need for standard representation, and in such a way as to enable mixed-discipline research with the sister field of various -omics technologies, an ISA-Tab "like" convention is adopted. Investigation, Study, and Assay are the three key entities around which the ISA-Tab framework is built [60,61]; these assist in structuring metadata and describing the relationship of samples to data.…”

Section: For Each Such Relationmentioning

confidence: 99%

A Novel Knowledge Representation Framework for the Statistical Validation of Quantitative Imaging Biomarkers

Buckler¹,

Paik

Ouellette³

et al. 2013

J Digit Imaging

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Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging

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The First RSBI (ISA-TAB) Workshop: “Can a Simple Format Work for Complex Studies?”

Cited by 81 publications

References 24 publications

Scientific Workflow Clustering Based on Motif Discovery

Scientific Workflow Clustering Based on Motif Discovery

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

A Novel Knowledge Representation Framework for the Statistical Validation of Quantitative Imaging Biomarkers

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