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At least 19 recordsLinked to original sources

PubMed1999

The PowerChecker: a visual basic program for ensuring data integrity.

Data entry and encoding errors can jeopardize the integrity of data sets generated in a variety of research settings. Despite researchers' pursuits of more accurate entry methods, data entry errors persist. Although techniques exist for identifying such errors, the PowerChecker program described here provides a more efficient method of data set validation. Rather than enter data twice and then manually search for the correct values when there are discrepancies between the two sets, with PowerChecker the user can correct entry errors as the data are entered the second time. In addition, the time-stamped record of changes to the original data set aids in meeting quality assurance requirements of the Good Clinical Practice standards.

Data Interpretation, Statistical↗

PubMed2005

YeastHub: a semantic web use case for integrating data in the life sciences domain.

MOTIVATION: As the semantic web technology is maturing and the need for life sciences data integration over the web is growing, it is important to explore how data integration needs can be addressed by the semantic web. The main problem that we face in data integration is a lack of widely-accepted standards for expressing the syntax and semantics of the data. We address this problem by exploring the use of semantic web technologies-including resource description framework (RDF), RDF site summary (RSS), relational-database-to-RDF mapping (D2RQ) and native RDF data repository-to represent, store and query both metadata and data across life sciences datasets. RESULTS: As many biological datasets are presently available in tabular format, we introduce an RDF structure into which they can be converted. Also, we develop a prototype web-based application called YeastHub that demonstrates how a life sciences data warehouse can be built using a native RDF data store (Sesame). This data warehouse allows integration of different types of yeast genome data provided by different resources in different formats including the tabular and RDF formats. Once the data are loaded into the data warehouse, RDF-based queries can be formulated to retrieve and query the data in an integrated fashion. AVAILABILITY: The YeastHub website is accessible via the following URL: http://yeasthub.gersteinlab.org.

Biology↗

PubMed2003

Maintaining data integrity in microarray data management.

Gene expression microarrays are a relatively new technology, dating back just a few years, yet they have already become a very widely used tool in biology, and have evolved to a wide range of applications well beyond their original design intent. However, while the use of microarrays has expanded, and the issues of performance optimization have been intensively studied, the fundamental issue of data integrity management has largely been ignored. Now that performance has improved so greatly, the shortcomings of data integrity control methods constitute a greater percent of the stumbling blocks for investigators. Microarray data are cumbersome, and the rule up to this point has mostly been one of hands-on transformations, leading to human errors which often have dramatic consequences. We show in this review that the time lost on such mistakes is enormous and dramatically affects results; therefore, mistakes should be mitigated in any way possible. We outline the scope of the data integrity issue, to survey some of the most common and dangerous data transformations, and their shortcomings. To illustrate, we review some case studies. We then look at the work done by the research community on this issue (which admittedly is meager up to this point). Some data integrity issues are always going to be difficult, while others will become easier-one of our goals is to expedite the use of integrity control methods. Finally, we present some preliminary guidelines and some specific approaches that we believe should be the focus of future research.

Algorithms↗

PubMed2002

Biological data integration: wrapping data and tools.

Nowadays scientific data is inevitably digital and stored in a wide variety of formats in heterogeneous systems. Scientists need to access an integrated view of remote or local heterogeneous data sources with advanced data accessing, analyzing, and visualization tools. Building a digital library for scientific data requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web as well as data generated by software. We present an approach to wrapping web data sources, databases, flat files, or data generated by tools through a database view mechanism. Generally, a wrapper has two tasks: it first sends a query to the source to retrieve data and, second builds the expected output with respect to the virtual structure. Our wrappers are composed of a retrieval component based on an intermediate object view mechanism called search views mapping the source capabilities to attributes, and an eXtensible Markup Language (XML) engine, respectively, to perform these two tasks. The originality of the approach consists of: 1) a generic view mechanism to access seamlessly data sources with limited capabilities and 2) the ability to wrap data sources as well as the useful specific tools they may provide. Our approach has been developed and demonstrated as part of the multidatabase system supporting queries via uniform object protocol model (OPM) interfaces.

Algorithms↗

PubMed1994

[The concept of a common medical information space: a new technology of integrated data for the improvement of health].

The concept of the common medical information space proposes that there are available health data kept in the data bases of various automatic medical systems. This data integration is achieved by integrating computers into the network, which will make a regular exchange of data among the functionally related medical institutions. The above concept of the common medical informational space is a basis for organizing a comprehensive monitoring for the health status of children (including the ecomedical aspect) at the basically new technological level, creating conditions for practical implementation of the concept of the continuum of the developing body's states, which will take into account the influence of many interacting factors on the developing fetus and child.

Medical Informatics↗

PubMed2005

BioMediator data integration: beyond genomics to neuroscience data.

The BioMediator system developed at the University of Washington (UW) provides a theoretical and practical foundation for data integration across diverse biomedical research domains and various data types. In this paper we demonstrate the generalizability of its architecture through its application to the UW Human Brain Project (HBP) for understanding language organization in the brain. We first describe the system architecture and the characteristics of the four data sources developed by the UW HBP. Second we present the process of developing the application prototype for HBP neuroscience researchers posing queries across these semantically and syntactically heterogeneous neurophysiologic data sources. Then we discuss the benefits and potential limitations of the BioMediator system as a general data integration solution for different user groups in genomic and neuroscience research domains.

Brain↗

PubMed2005

Data integration: challenges for drug discovery.

The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies - challenges that could require significant methodological and even cultural changes in our approach to data.

Data Collection↗

PubMed2001

A model for data integration systems of biomedical data applied to online genetic databases.

We present a general model for data integration systems using a mediated schema to represent commonalities in the underlying sources. These sources are mapped to the mediated schema using source descriptions. Users can pose queries against the mediated schema, allowing the system to generate automatically a query plan that enumerates and ranks all possible ways in which the query could be answered. We apply this approach to the domain of online genetic databases, demonstrating the system s ability to answer relevant queries across multiple sources.

Databases, Genetic↗

PubMed2005

A data integration methodology for systems biology: experimental verification.

The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein-DNA and protein-protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein-protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets.

Chromatin Immunoprecipitation↗

PubMed2005

Biomedical data integration: using XML to link clinical and research data sets.

Data integration occurs when a query proceeds through multiple data sets, thereby relating diverse data extracted from different data sources. Data integration is particularly important to biomedical researchers since data obtained from experiments on human tissue specimens have little applied value unless they can be combined with medical data (i.e., pathologic and clinical information). In the past, research data were correlated with medical data by manually retrieving, reading, assembling and abstracting patient charts, pathology reports, radiology reports and the results of special tests and procedures. Manual annotation of research data is impractical when experiments involve hundreds or thousands of tissue specimens resulting in large, complex data collections. The purpose of this paper is to review how XML (eXtensible Markup Language) provides the fundamental tools that support biomedical data integration. The article also discusses some of the most important challenges that block the widespread availability of annotated biomedical data sets.

Data Collection↗

PubMed1997

Web-based data integration and annotation in the intensive care unit.

Integrating patient data to facilitate review and annotation is a challenging task in the intensive care unit (ICU), partially due to the wide variety of proprietary systems and types of data involved. This paper describes SIMON-Web, a Java-based user interface to integrate data from an existing bedside monitoring system and a clinical information system (CIS). SIMON-Web displays graphical data from physiologic monitors and other bedside devices as well as information from the clinical laboratory and physician order-entry systems, and allows users to annotate the data using a point-and-click or free-text interface. By continuing to add functionality to SIMON-Web's extensible user interface, we hope to continue to augment and eventually replace manual care provider data charting in the ICU. As of March, 1997, SIMON-Web has been implemented on two bedside personal computer workstations in the cardiac care unit (CCU) at Vanderbilt University Medical Center.

Computer Communication Networks↗

PubMed2005

Assessing the limits of genomic data integration for predicting protein networks.

Genomic data integration--the process of statistically combining diverse sources of information from functional genomics experiments to make large-scale predictions--is becoming increasingly prevalent. One might expect that this process should become progressively more powerful with the integration of more evidence. Here, we explore the limits of genomic data integration, assessing the degree to which predictive power increases with the addition of more features. We focus on a predictive context that has been extensively investigated and benchmarked in the past-the prediction of protein-protein interactions in yeast. We start by using a simple Naive Bayes classifier for integrating diverse sources of genomic evidence, ranging from coexpression relationships to similar phylogenetic profiles. We expand the number of features considered for prediction to 16, significantly more than previous studies. Overall, we observe a small, but measurable improvement in prediction performance over previous benchmarks, based on four strong features. This allows us to identify new yeast interactions with high confidence. It also allows us to quantitatively assess the inter-relations amongst different genomic features. It is known that subtle correlations and dependencies between features can confound the strength of interaction predictions. We investigate this issue in detail through calculating mutual information. To our surprise, we find no appreciable statistical dependence between the many possible pairs of features. We further explore feature dependencies by comparing the performance of our simple Naive Bayes classifier with a boosted version of the same classifier, which is fairly resistant to feature dependence. We find that boosting does not improve performance, indicating that, at least for prediction purposes, our genomic features are essentially independent. In summary, by integrating a few (i.e., four) good features, we approach the maximal predictive power of current genomic data integration; moreover, this limitation does not reflect (potentially removable) inter-relationships between the features.

Algorithms↗

PubMedDate not supplied

Maintaining data integrity in randomized clinical trials.

BACKGROUND: The process of attaining and maintaining data integrity is critical to ensure a successful randomized clinical trial. Methodologic strategies to achieve data integrity when repeated measures are used has not been discussed in detail in the literature. The National Institutes of Health requires that data integrity and safety monitoring boards or plans be established for randomized clinical trials. OBJECTIVES: The objectives of this paper are to (a) examine important data collection issues nurse scientists often encounter in randomized clinical trials and (b) present a process that researchers can apply to achieve data integrity. METHODS: The process to achieve data integrity is based on strategies that were developed by an interdisciplinary hospice research team involved in an ongoing National Institutes of Health-funded clinical trial. The process and key issues are illustrated with methodologic examples from the randomized clinical trial and supporting literature. RESULTS: The process of achieving data integrity involves developing protocols in three key areas: data collection, training of data collectors, and data monitoring. The use of these protocols will increase the rigor of the clinical trial and assist in maintaining study validity. CONCLUSIONS: Investigators conducting clinical trials need to consider all issues involved in achieving data integrity and have tested protocols in place throughout the study. These approaches will not only help maintain study validity but also help ensure data of sufficient quantity and quality to achieve the desired statistical power.

Bias↗

PubMed1998

The Merck Gene Index browser: an extensible data integration system for gene finding, gene characterization and EST data mining.

MOTIVATION: To make effective use of the vast amounts of expressed sequence tag (EST) sequence data generated by the Merck-sponsored EST project and other similar efforts, sequences must be organized into gene classes, and scientists must be able to 'mine' the gene class data in the context of related genomic data. RESULTS: This paper presents the Merck Gene Index browser, an easily extensible, World Wide Web-based system for mining the Merck Gene Index (MGI) and related genomic data. The MGI is a non-redundant set of clones and sequences, each representing a distinct gene, constructed from all high-quality 3' EST sequences generated by the Merck-sponsored EST project. The MGI browser integrates data from a variety of sources and storage formats, both local and remote, using an eclectic integration strategy, including a federation of relational databases, a local data warehouse and simple hypertext links. Data currently integrated include: LENS cDNA clone and EST data, dbEST protein and non-EST nucleic acid similarity data, WashU sequence chromatograms. Entrez sequence and Medline entries, and UniGene gene clusters. Flatfile sequence data are accessed using the Bioapps server, an internally developed client-server system that supports generic sequence analysis applications. Browser data are retrieved and formatted by means of the Bioinformatics Data Integration Toolkit (B-DIT), a new suite of Perl routines.

Abstracting and Indexing↗

PubMed1999

Data integration to assist gastric emptying data analysis.

The diagnosis of dyspepsia is very difficult because the symptoms are clinically aspecific and the gastric emptying time tests are of complex interpretation. An integrated and automated analysis of clinical and instrumental data may improve the diagnostic process. We present a system to collect data on dyspeptic patient from different sources which has been set up to assist the clinician in the diagnosis of dyspepsia. The data base integrates a wide set of symptoms with data coming from laboratory tests. Moreover, we assess the feasibility of classifying gastric emptying profiles using both octanoid acid excretion data and electrogastrography.

Adult↗

PubMed2002

Data integration in the evaluation of juvenile justice education.

This article discusses the Juvenile Justice Educational Enhancement Program's evaluation of juvenile justice education through the integration of multiple data sources. Validation of best practices and the corresponding effect of these practices on community reintegration require the development of a comprehensive database of program-level and individual-level indicators from several sources. These sources include the Florida Department of Education, the Florida Department of Juvenile Justice, the Florida Department of Law Enforcement, the Florida Department of Corrections, and the Juvenile Justice Educational Enhancement Program's own educational quality assurance and pre-/posttest data. This article examines the methodological, political, and bureaucratic obstacles encountered in this effort. Evaluation research analyses are also explored with an emphasis on the meaning of outcome measures relative to causal issues of time order, the modeling of events, and analytic strategy. The article provides a framework for future juvenile justice educational evaluation and a critical examination of problems encountered in integrating data sources for the purpose of outcome evaluation.

Criminal Law↗

PubMed1996

The benefits of data integration: HFMA study findings.

An HFMA study of "best practices" integrated delivery systems that have integrated their clinical and financial data has found that these systems employ some common strategies--they buy rather than build their system software and they strictly adhere to industry standards for EDI and data conversion. The data integration study also shows that best practices systems are concentrating more on automating ambulatory care processes rather than their inpatient care processes and on developing comprehensive order-entry systems. Study findings were used to refine a conceptual data model that healthcare financial managers can use to implement integrated data collection activities in their organizations.

Ambulatory Care Information Systems↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗