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PubMed2006

Study of noise propagation and the effects of insufficient numbers of projection angles and detector samplings for iterative reconstruction using planar-integral data.

A rotating slat collimator can be used to acquire planar-integral data. It achieves higher geometric efficiency than a parallel-hole collimator by accepting more photons, but the planar-integral data contain less tomographic information that may result in larger noise amplification in the reconstruction. Lodge evaluated the rotating slat system and the parallel-hole system based on noise behavior for an FBP reconstruction. Here, we evaluate the noise propagation properties of the two collimation systems for iterative reconstruction. We extend Huesman's noise propagation analysis of the line-integral system to the planar-integral case, and show that approximately 2.0(D/dp) SPECT angles, 2.5(D/dp) self-spinning angles at each detector position, and a 0.5dp detector sampling interval are required in order for the planar-integral data to be efficiently utilized. Here, D is the diameter of the object and dp is the linear dimension of the voxels that subdivide the object. The noise propagation behaviors of the two systems are then compared based on a least-square reconstruction using the ratio of the SNR in the image reconstructed using a planar-integral system to that reconstructed using a line-integral system. The ratio is found to be proportional to the square root of F/D, where F is a geometric efficiency factor. This result has been verified by computer simulations. It confirms that for an iterative reconstruction, the noise tradeoff of the two systems is not only dependent on the increase of the geometric efficiency afforded by the planar projection method, but also dependent on the size of the object. The planar-integral system works better for small objects, while the line-integral system performs better for large ones. This result is consistent with Lodge's results based on the FBP method.

Artifacts↗
PubMed2005

A data integration methodology for systems biology.

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.

Informatics↗
PubMed2004

SMART 4.0: towards genomic data integration.

SMART (Simple Modular Architecture Research Tool) is a web tool (http://smart.embl.de/) for the identification and annotation of protein domains, and provides a platform for the comparative study of complex domain architectures in genes and proteins. The January 2004 release of SMART contains 685 protein domains. New developments in SMART are centred on the integration of data from completed metazoan genomes. SMART now uses predicted proteins from complete genomes in its source sequence databases, and integrates these with predictions of orthology. New visualization tools have been developed to allow analysis of gene intron-exon structure within the context of protein domain structure, and to align these displays to provide schematic comparisons of orthologous genes, or multiple transcripts from the same gene. Other improvements include the ability to query SMART by Gene Ontology terms, improved structure database searching and batch retrieval of multiple entries.

Algorithms↗
PubMed2025

AI-HOPE: an AI-driven conversational agent for enhanced clinical and genomic data integration in precision medicine research.

MOTIVATION: The growing complexity of clinical cancer research has fueled a surge in demand for automated bioinformatics tools capable of integrating clinical and genomic data to accelerate discovery efforts. RESULTS: We present the Artificial Intelligence Agent for High-Optimization and Precision Medicine (AI-HOPE), an AI-driven system that enables domain experts to conduct integrative data analyses through natural language interactions. Powered by Large Language Models, AI-HOPE interprets user instructions, converts them into executable code, and autonomously analyzes locally stored data. It supports flexible association studies, subset comparisons, clinical prevalence assessments and survival analyses. In addition, AI-HOPE enables global variable scans to identify features significantly associated with a user-defined outcome, making a powerful and intuitive tool for advancing precision medicine research. Importantly, its closed-system design prevents clinical data leakage. To demonstrate its utility, AI-HOPE was applied to The Cancer Genome Atlas data to address two clinical questions. First, it identified significant enrichment of TP53 mutations in late-stage colorectal cancer compared to early-stage cases. Second, it uncovered a strong association between KRAS mutations and poorer progression-free survival in FOLFOX-treated patients. These findings align with established literature and demonstrate AI-HOPE's ability to generate meaningful insights independently, without prior assumptions. By removing programming barriers and simplifying complex analyses, AI-HOPE bridges the gap between data complexity and research needs. With its scalable and adaptable framework, AI-HOPE has the potential to support diverse biomedical research fields, driving innovation and efficiency in translational studies. AVAILABILITY AND IMPLEMENTATION: The AI-HOPE software and demonstration data is available at https://github.com/Velazquez-Villarreal-Lab/AI-HOPE.

Precision Medicine↗
PubMed2006

Beyond the data deluge: data integration and bio-ontologies.

Biomedical research is increasingly a data-driven science. New technologies support the generation of genome-scale data sets of sequences, sequence variants, transcripts, and proteins; genetic elements underpinning understanding of biomedicine and disease. Information systems designed to manage these data, and the functional insights (biological knowledge) that come from the analysis of these data, are critical to mining large, heterogeneous data sets for new biologically relevant patterns, to generating hypotheses for experimental validation, and ultimately, to building models of how biological systems work. Bio-ontologies have an essential role in supporting two key approaches to effective interpretation of genome-scale data sets: data integration and comparative genomics. To date, bio-ontologies such as the Gene Ontology have been used primarily in community genome databases as structured controlled terminologies and as data aggregators. In this paper we use the Gene Ontology (GO) and the Mouse Genome Informatics (MGI) database as use cases to illustrate the impact of bio-ontologies on data integration and for comparative genomics. Despite the profound impact ontologies are having on the digital categorization of biological knowledge, new biomedical research and the expanding and changing nature of biological information have limited the development of bio-ontologies to support dynamic reasoning for knowledge discovery.

Animals↗
PubMedDate not supplied

Managing troubled data: coastal data partnerships smooth data integration.

Understanding the ecology, condition, and changes of coastal areas requires data from many sources. Broad-scale and long-term ecological questions, such as global climate change, biodiversity, and cumulative impacts of human activities, must be addressed with databases that integrate data from several different research and monitoring programs. Various barriers, including widely differing data formats, codes, directories, systems, and metadata used by individual programs, make such integration troublesome. Coastal data partnerships, by helping overcome technical, social, and organizational barriers, can lead to a better understanding of environmental issues, and may enable better management decisions. Characteristics of successful data partnerships include a common need for shared data, strong collaborative leadership, committed partners willing to invest in the partnership, and clear agreements on data standards and data policy. Emerging data and metadata standards that become widely accepted are crucial. New information technology is making it easier to exchange and integrate data. Data partnerships allow us to create broader databases than would be possible for any one organization to create by itself.

Conservation of Natural Resources↗
PubMed2006

Data integration and genomic medicine.

Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means data sets are large and highly heterogeneous. To create knowledge from data, researchers must integrate these large and diverse data sets. This presents daunting informatic challenges such as representation of data that is suitable for computational inference (knowledge representation), and linking heterogeneous data sets (data integration). Fortunately, many of these challenges can be classified as data integration problems, and technologies exist in the area of data integration that may be applied to these challenges. In this paper, we discuss the opportunities of genomic medicine as well as identify the informatics challenges in this domain. We also review concepts and methodologies in the field of data integration. These data integration concepts and methodologies are then aligned with informatics challenges in genomic medicine and presented as potential solutions. We conclude this paper with challenges still not addressed in genomic medicine and gaps that remain in data integration research to facilitate genomic medicine.

Biomedical Research↗
PubMed2026

Scalable, open-access and multidisciplinary data integration pipeline for climate-sensitive diseases.

Climate-sensitive infectious diseases pose an important challenge for human, animal and environmental health and it has been estimated that over half of known human pathogenic diseases can be aggravated by climate change. While climatic and weather conditions are important drivers of transmission of vector-borne diseases, socio-economic, behavioural, and land-use factors as well as the interactions among them impact transmission dynamics. Analysis of drivers of climate-sensitive diseases require rapid integration of interdisciplinary data to be jointly analysed with epidemiological (including genomic and clinical) data. Current tools for the integration of multiple data sources are often limited to one data type or rely on proprietary data and software. To address this gap, we develop a scalable and open-access pipeline for the integration of multiple spatio-temporal datasets that requires only the declaration of the country and temporal range and resolution of the study. The tool is locally deployable and can easily be integrated into existing climate-disease-modelling applications. We demonstrate the utility of the tool for dengue modelling in Vietnam where epidemiological data are legally required to remain local. We include a pipeline for bias correction of climate data to enhance their quality for downstream modelling tasks. The Dengue Advanced Readiness Tools-Pipeline empowers users by simplifying complex download, correction, and aggregation steps, fostering data-driven discovery of relationships between infectious diseases and their drivers in space and time, and enhancing reproducibility in research. Additional modules and datasets can be added to the existing ones to make the pipeline extendable to use cases other than the ones presented here.

automated workflows↗
PubMed2000

An integrated data warehouse system: development, implementation, and early outcomes.

This paper describes a generic vision of global information flow and the development of an integrated data warehouse system, using clinical data on all patient encounters and administrative data on all operating transactions as part of an integrated health care system. This new integrated data warehouse system has been successfully used for multiple purposes, including patient care, health services research, resource utilization and feasibility studies. During 1999, core analyses included the electronic abstraction, aggregation, and analysis of data on over 400,000 patients. This approach to building a centralized data system comprised of multiple repositories efficiently meets a variety of individual and aggregate information needs, while reducing the need to create duplicate databases.

Database Management Systems↗
PubMed1992

The organization engine: virtual data integration.

The Organization Engine is an early example of Virtual Data Integration--providing the appearance of integration at the desktop without modifying existing infrastructure. Starting with the Organization Engine, eight programming days were needed to provide uniform desktop access to a CODASYL-compliant hospital information system and to a MUMPS-based radiology information system (the technique is equally effective for relational and other data bases). The resulting tool provides a seamless integration of these two systems, image storage, pre-recorded audio, and document storage. In addition to providing uniform access, the tool allows healthcare providers to organize the data to suit their individual needs. The ease of this integration lies in two simple techniques: the transformation of data from all sources into a single, homogeneous representation, and the use of simple customization files to describe new object types and formats. The approach is sufficiently general to allow the integration of applications which present external interfaces of radically different forms. Two such forms are discussed here: data map publication and transactions.

Computer Communication Networks↗
PubMed2005

Quality management tools: facilitating clinical research data integrity by utilizing specialized reports with electronic case report forms.

Data collected throughout the course of a clinical research trial must be reviewed for accuracy and completeness continually. The Oracle Clinical (OC) data management application utilized to capture clinical data facilitates data integrity through pre-programmed validations, edit and range checks, and discrepancy management modules. These functions were not enough. Coupled with the use of specially created reports in Oracle Discoverer and Integrated Review, both ad-hoc query and reporting tools, research staff have enhanced their ability to clean, analyze and report more accurate data captured within and among Case Report Forms (eCRFs) by individual study or across multiple studies.

Biomedical Research↗
PubMedDate not supplied

Integrating data from legacy systems using object linking and embedding technology: development of a reporting system for heavy metal poisoning results.

Integrating data that reside in different systems remains an often laborious process, requiring either manual steps or complicated programming. This paper describes a method for state-mandated reporting of childhood blood lead testing results that makes use of object linking and embedding technology and readily available software products to pull together information from different legacy systems. A terminal session emulator employs object linking and embedding automation to extract host data, and Visual Basic routines specify the user interface and database manipulation. This system has significantly increased the efficiency and accuracy with which blood lead testing reports are provided to the local state health department. The system provides a model for a relatively easy solution for laboratories and other groups that need a way to integrate standard data sets that are distributed across legacy systems.

Child↗
PubMed2004

GDPC: connecting researchers with multiple integrated data sources.

UNLABELLED: The goal of this project is to simplify access to genomic diversity and phenotype data, thereby encouraging reuse of this data. The Genomic Diversity and Phenotype Connection (GDPC) accomplishes this by retrieving data from one or more data sources and by allowing researchers to analyze integrated data in a standard format. GDPC is written in JAVA and provides (1) data sources available as web services that transfer XML formatted data via the SOAP protocol; (2) a JAVA API for programmatic access to data sources; and (3) a front-end application that allows users to manage data sources, retrieve data based on filters, sort/group data based on property values and save/open the data as XML files. AVAILABILITY: The source code, compiled code, documentation and GDPC Browser are freely available at: www.maizegenetics.net/gdpc/index.html the current release of GDPC is version 1.0, with updated releases planned for the future. Comments are welcome.

Database Management Systems↗
PubMed1990

A comparison of two methods for facilitating clinical data integration by medical students.

Two important charting strategies to help students organize patients' data are Weed's problem-oriented medical record (POMR) and Russell's condition diagram (CD). The authors conducted the present study in 1987 to determine whether either was superior for clinical data integration. Sophomore medical students at The University of Texas Medical School at San Antonio indicated whether they preferred the POMR, the CD, or neither. They were then divided into three study groups according to their preferences, with the POMR and CD groups receiving 80 hours of training and the control group receiving only the standard preclinical training. Each student then examined a standardized patient and wrote an open-ended report about the patient's medical problem. After examining a second patient, students were asked to write a structured report providing information about each of ten components of diagnosis. Both the CD and the POMR groups scored numerically higher on the structured type of report than did the controls, but only the CD group scored significantly higher. The CD group also scored higher than did the POMR group on both types of report, but the differences were not statistically significant. This study indicates that the clinical reasoning of medical students can be enhanced by focused training in either the CD or the POMR methods. It suggests that the CD format may be particularly helpful for students with lower academic achievement.

Algorithms↗
PubMedDate not supplied

Ensuring data quality in medical research through an integrated data management system.

An effective data management system ensures high quality research data by making certain of the proper execution of the study design. This paper presents the components of a data management system and describes procedures for use in each component of the system to obtain high quality data. We discuss the interrelationship among the components of the data management system and the relationship of the data management system to other parts of the research project. We identify underlying principles in design and implementation of a data management system to ensure high quality data.

Computers↗
PubMed2003

Realization of real-time clinical data integration using advanced database technology.

As information & communication technologies have advanced, interest in mobile health care systems has grown. In order to obtain information seamlessly from distributed and fragmented clinical data from heterogeneous institutions, we need solutions that integrate data. In this article, we introduce a method for information integration based on real-time message communication using trigger and advanced database technologies. Messages were devised to conform to HL7, a standard for electronic data exchange in healthcare environments. The HL7 based system provides us with an integrated environment in which we are able to manage the complexities of medical data. We developed this message communication interface to generate and parse HL7 messages automatically from the database point of view. We discuss how easily real time data exchange is performed in the clinical information system, given the requirement for minimum loading of the database system.

Clinical Laboratory Information Systems↗
PubMed2004

Data integrity: beware of viruses.

School nurses and health office employees are the creators and caretakers of legal documentation. School nurses have an ethical and legal obligation to protect the integrity of electronic student health records. Although there are many threats to data integrity, from inadequate hardware to electrical surges, one of the most pervasive threats to data is computer viruses. There are many precautions that can be taken to protect electronic student health data from viruses in the school health office.

Computer Security↗