XML-based approaches for the integration of heterogeneous bio-molecular data

Mesiti, Marco; Jiménez-Ruiz, Ernesto; Sanz, Ismael; Berlanga, Rafael; Perlasca, Paolo; Valentini, Giorgio; Manset, David

doi:10.1186/1471-2105-10-s12-s7

Cited by 23 publications

(6 citation statements)

References 96 publications

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“…The fact that Q2 includes measures of scan quality is potentially just as important for clinicians as for the decision support system, analogous to the way image quality measures are useful in image databases for teaching (22). For exchanging data between databases, we use XML, a text-based format developed to facilitate data interchange between applications (23). …”

Section: Discussionmentioning

confidence: 99%

Development of a Relational Database to Capture and Merge Clinical History with the Quantitative Results of Radionuclide Renography

Folks

Savir-Baruch

Garcia

et al. 2012

Journal of Nuclear Medicine Technology

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Our objective was to design and implement a clinical history database capable of linking to our database of quantitative results from 99mTc-mercaptoacetyltriglycine (MAG3) renal scans and export a data summary for physicians or our software decision support system. Methods For database development, we used a commercial program. Additional software was developed in Interactive Data Language. MAG3 studies were processed using an in-house enhancement of a commercial program. The relational database has 3 parts: a list of all renal scans (the RENAL database), a set of patients with quantitative processing results (the Q2 database), and a subset of patients from Q2 containing clinical data manually transcribed from the hospital information system (the CLINICAL database). To test interobserver variability, a second physician transcriber reviewed 50 randomly selected patients in the hospital information system and tabulated 2 clinical data items: hydronephrosis and presence of a current stent. The CLINICAL database was developed in stages and contains 342 fields comprising demographic information, clinical history, and findings from up to 11 radiologic procedures. A scripted algorithm is used to reliably match records present in both Q2 and CLINICAL. An Interactive Data Language program then combines data from the 2 databases into an XML (extensible markup language) file for use by the decision support system. A text file is constructed and saved for review by physicians. Results RENAL contains 2,222 records, Q2 contains 456 records, and CLINICAL contains 152 records. The interobserver variability testing found a 95% match between the 2 observers for presence or absence of ureteral stent (κ = 0.52), a 75% match for hydronephrosis based on narrative summaries of hospitalizations and clinical visits (κ = 0.41), and a 92% match for hydronephrosis based on the imaging report (κ = 0.84). Conclusion We have developed a relational database system to integrate the quantitative results of MAG3 image processing with clinical records obtained from the hospital information system. We also have developed a methodology for formatting clinical history for review by physicians and export to a decision support system. We identified several pitfalls, including the fact that important textual information extracted from the hospital information system by knowledgeable transcribers can show substantial interobserver variation, particularly when record retrieval is based on the narrative clinical records.

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Section: Discussionmentioning

confidence: 99%

Development of a Relational Database to Capture and Merge Clinical History with the Quantitative Results of Radionuclide Renography

Folks

Savir-Baruch

Garcia

et al. 2012

Journal of Nuclear Medicine Technology

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“…Other methods differentially weight each data source using techniques ranging from Gaussian random fields [ 46 ] to the naive-Bayes integration [ 157 ] and constrained linear regression [ 14 ], or by merging data taking into account the GO hierarchy [ 158 ], or by applying XML-based techniques [ 159 ].…”

Section: The Hierarchical Classification Alone Is Not Enoughmentioning

confidence: 99%

Hierarchical Ensemble Methods for Protein Function Prediction

Valentini

2014

ISRN Bioinformatics

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Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research.

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“…The weakness in [32] is addressed in [33] to incorporate support of enumerations, list and union type constructors, with a proprietary approach. In [34], a platform independent logical schema is used to provide direct mapping using a UML profile. The coverage of XML Schema concepts is broad, but several mappings do not conform to UML.…”

Section: Related Workmentioning

confidence: 99%

Semantically Interoperable XML Data

Vergara-Niedermayr

Wang

Pan

et al. 2013

Int. J. Semantic Computing

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XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups.

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XML-based approaches for the integration of heterogeneous bio-molecular data

Cited by 23 publications

References 96 publications

Development of a Relational Database to Capture and Merge Clinical History with the Quantitative Results of Radionuclide Renography

Development of a Relational Database to Capture and Merge Clinical History with the Quantitative Results of Radionuclide Renography

Hierarchical Ensemble Methods for Protein Function Prediction

Semantically Interoperable XML Data

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