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PubMed1979

Women and health statistics: areas of deficient data collection and integration.

Improved data on women's health should be based on a definition of health capital for women that takes into account reproductive efficiency and the life experience of women in relation to fertility, general health, employment, and household responsibilities. More information is needed about the conduct of household roles related to investment in health capital for family members. Definitions of health are pragmatic and are related to social systems, including health care providers, for which the definitions serve as signals for certain actions. Women can seek to modify these definitions, which have expressed historical biases about women. Data collection should include information about the extent to which privacy as a dimension of access to health care is achieved. Longitudinal and other approaches to health history data should be explored. Fertility and general health data statistics in federal data systems need to be integrated. Insurance adequacy must be determined in relation to benefit scope, relation to prices, employment requirements, and family contracts. Data that will be helpful in adapting industrial environment to health goals are needed. Provider characteristics relevant to women as patients and workers, and qualities of residential environments pertaining to health, need to be studied. Future research should include evaluation of women's health capital and opportunities for programmatic investment to improve household efficiency in conduct of health-related affairs.

Employment↗
PubMed2006

Managing data for integrity: policies and procedures for ensuring the accuracy and quality of the data in the laboratory.

Management of the research data is an extremely important responsibility of the Principal Investigator (PI) and other members of the research team. Without accurate data, no worthwhile conclusions can be drawn from the research study. Integrity in data management is critical to the success of the research group and to public trust in the research outcomes. One of the primary responsibilities of the PI is to provide proper training to the junior members of the lab. This effort can be buttressed by institutional data policies that are implemented at the group level. Extensive and frequent guidance in good research practices by the PI and other senior research staff is critical to the proper training of new scientists.

Data Collection↗
PubMed2001

Bioinformatics for rice resources.

The distinguishing feature of the 'new biology' is that it is information intensive. Not only does it demand access to and assimilation of vast data sets accumulated by engineered laboratory processes, but it also demands a previously unimaginable level of data integration across data types and sources. There are various information resources available for rice. In addition, there are various information resources that are not focused on rice but that contain rice data. The challenge for rice researchers and breeders is to access this wealth of data meaningfully. This challenge will grow significantly as international efforts aimed at sequencing the entire rice genome come into full swing. Only through concerted efforts in bioinformatics will the power of these public data be brought to bear on the needs of rice researchers and breeders worldwide. These efforts will need to focus on two large but distinct areas: (1) development of an effective bioinformatics infrastructure (hardware systems, software systems, and software engineers and support staff) and (2) computational biology research in visualization and analysis of very large, complex data sets, such as those that will be developed using high-throughput expression technologies, large-scale insertional mutagenesis, and biochemical profiling of various types. In the midst of the large flow of high-throughput data that the international rice genome sequencing efforts will produce, it is also imperative that integration of those data with unique germplasm data held in trust by the CGIAR be a part of the informatics infrastructure. This paper will focus on the state of rice information resources, the needs of the rice community, and some proposed bioinformatics activities to support these needs.

Algorithms↗
PubMed1993

Metaphor graphics to support integrated decision making with respiratory data.

Support for data integration in the intensive care unit (ICU) often includes efforts to improve the display of data. An electronic version of a flowsheet (table of numbers) with optimal line graphs is by far the most common format in current ICU computer workstation technology, yet there is little evidence that this format provides particularly good support for human integration of data. The present work introduces a new form of graphic representation, one that is far more metaphoric, far more tailored to the intensive care unit than a line graph. This graphic system, called volume rectangles represents mechanical ventilator data in such a way that is easy to keep different types of variables conceptually separated, yet easy to see how they relate in a truly integrated way. Volume rectangles are one example of a general approach to display of data called the metaphor graphic approach, which is being evaluated in this and other contexts. Metaphor graphics are custom tailored visual displays designed to look like the real world situation from which the data is collected but not in a literal sense of 'look like'. Anecdotal observation suggests that such graphics are easy to learn, are remembered over long period, and are good decision support tools when the task is finding patterns in a mass of data.

Artificial Intelligence↗
PubMedDate not supplied

Required steps for the validation of a Laboratory Information Management System.

The task of managing laboratory data is not a new one. Over the past two decades, the use of Laboratory Information Management Systems (LIMS) has revolutionized how laboratories manage their data. A LIMS is more than software; it has become the workhorse of the laboratory, encompassing laboratory work-flow combined with user input, data collection, instrument integration, data analysis, user notification, and delivery of information and reporting. Types of organizations that utilize LIMS vary greatly from research laboratories to manufacturing laboratories to environmental testing laboratories. Commercially-available LIMS have been around since the 1980s. In addition, many laboratories have designed, implemented, and maintained in-house LIMS. The heart of any LIMS is the software. Like other laboratory systems, the LIMS software is subject to quality control and quality assurance checks. In regulatory environments this associated QA/QC is referred to as "system validation." The primary purpose of system validation is to ensure that the software is performing in a manner for which it was designed. For example, the system acceptance criteria should be established and tested against quantifiable tasks to determine if the desired outcome has been achieved. LIMS features, such as autoreporting, reproducibility, throughput, and accuracy must be quantifiable and verifiable. System validation ensures that the entire system has been properly tested, incorporates required controls, and maintains and will continue to maintain data integrity. Laboratories must establish protocols and standards for the validation process and associated documentation. Although vendors of commercial LIMS perform initial internal system validations, the system must be revalidated whenever the end user, vendor or third party adds modifications or customizations to the LIMS. Currently, detailed guidance regarding system validation of LIMS is not available to the user. The issue is addressed in Good Automated Laboratory Practices (GALP) and National Environmental Laboratory Accreditation Conference (NELAC) documents which indicate specific requirements or recommendations for operational checks and periodic testing; however, it is up to the laboratory to determine suitable methods to accomplish these tasks. Proper validation of a LIMS will allow a laboratory to comply with regulations and also provide comprehensive documentation on the system that is necessary to troubleshoot future problems.

Clinical Laboratory Information Systems↗
PubMed2006

Integrating image data into biomedical text categorization.

Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classification, and provided training and test data sets. So far, these efforts focused on analyzing only the text content of documents. However, as was noted in the KDD'02 text mining contest-where figure-captions proved to be an invaluable feature for identifying documents of interest-images often provide curators with critical information. We examine the possibility of using information derived directly from image data, and of integrating it with text-based classification, for biomedical document categorization. We present a method for obtaining features from images and for using them-both alone and in combination with text-to perform the triage task introduced in the TREC Genomics track 2004. The task was to determine which documents are relevant to a given annotation task performed by the Mouse Genome Database curators. We show preliminary results, demonstrating that the method has a strong potential to enhance and complement traditional text-based categorization methods.

Artificial Intelligence↗
PubMed2006

HKL-3000: the integration of data reduction and structure solution--from diffraction images to an initial model in minutes.

A new approach that integrates data collection, data reduction, phasing and model building significantly accelerates the process of structure determination and on average minimizes the number of data sets and synchrotron time required for structure solution. Initial testing of the HKL-3000 system (the beta version was named HKL-2000_ph) with more than 140 novel structure determinations has proven its high value for MAD/SAD experiments. The heuristics for choosing the best computational strategy at different data resolution limits of phasing signal and crystal diffraction are being optimized. The typical end result is an interpretable electron-density map with a partially built structure and, in some cases, an almost complete refined model. The current development is oriented towards very fast structure solution in order to provide feedback during the diffraction experiment. Work is also proceeding towards improving the quality of phasing calculation and model building.

Crystallography, X-Ray↗
PubMed2005

Integrated nuclear data utilisation system for innovative reactors.

A five-year research and development project on an integrated nuclear data utilisation system was initiated in 2002, for developing innovative nuclear energy systems such as accelerator-driven systems. The integrated nuclear data utilisation system will be constructed as a modular code system, which consists of two sub-systems: the nuclear data search and plotting sub-system, and the nuclear data processing and utilisation sub-system. The system will be operated with a graphical user interface in order to enable easy utilisation through the Internet by both nuclear design engineers and nuclear data evaluators. This paper presents an overview of the integrated nuclear data utilisation system, describes the development of a prototype system to examine the operability of the user interface and discusses specifications of the two sub-systems.

Computer Graphics↗
PubMed2026

Investing in Canada's nursing workforce: a comprehensive review to inform policy innovations and directions.

BACKGROUND: Health systems worldwide face persistent health workers challenges including nursing shortages, workforce strain, and inequities. In Canada, these challenges have prompted renewed national and provincial reforms to strengthen recruitment, retention, leadership, and sustainability. This paper compares nursing workforce policy directions across Canada, and international jurisdictions to inform policy and planning. METHODS: A cross-country comparative analysis of policies building on a comprehensive national funded review that included an umbrella review of 69 systematic reviews, a comparative policy review of nursing workforce strategies in five jurisdictions, and validation through national horizon-scanning and policy dialogues (n >100). Evidence was analyzed across system, organizational, and individual levels. RESULTS: At the system level, international jurisdictions demonstrate comprehensive, legislated approaches integrating data, governance, and multi-year funding have advanced key nursing strategies. In Canada, the advances show the importance of strategies to have national and provincial/territorial alignment emphasizing leadership, flexibility, and inclusion as key levers. Organizational and individual-level reforms such as mentorship, leadership development, and wellness initiatives are expanding but remain variably evaluated. Experts identified national workforce data strategies and policy integration with embedded evaluation as key enablers to inform scalability and sustainability of implemented strategies. CONCLUSIONS: Canada's nursing workforce reforms are advancing toward coordinated, equity-driven, and evidence-informed strategies. Continued investment in evaluation, leadership, and national integrated data systems along with integrating nursing workforce planning within broader intersectoral planning will consolidate these gains and position Canada as an international leader in sustainable nursing workforce policy.

Canada↗
PubMed2002

Integrating sibship data for mapping quantitative trait loci.

Sibship methods have been shown to be more powerful than traditional sib-pair methods in mapping quantative trait loci. We propose a statistical procedure which integrates data on sibships into a so-called 'contrast function', a natural extension of the classical squared sib-pair trait difference proposed by Haseman & Elston (1972). We also develop a combined mean and contrast function which provides more information on linkage compared to the contrast function. Our method is extended to multiple, epistatically interacting trait loci. Monte-Carlo simulations are included to compare the efficiencies of the proposed procedures with some currently used methods. An application of our proposed method is presented using data on alcohol dependence.

Alcoholism↗
PubMed1991

Providing an integrated clinical data view in a hospital information system that manages multimedia data.

The VA's hospital information system, the Decentralized Hospital Computer Program (DHCP), is an integrated system based on a powerful set of software tools with shared data accessible from any of its application modules. It includes many functionally specific application subsystems such as laboratory, pharmacy, radiology, and dietetics. Physicians need applications that cross these application boundaries to provide useful and convenient patient data. One of these multi-specialty applications, the DHCP Imaging System, integrates multimedia data to provide clinicians with comprehensive patient-oriented information. User requirements for cross-disciplinary image access can be studied to define needs for similar text data access. Integration approaches must be evaluated both for their ability to deliver patient-oriented text data rapidly and their ability to integrate multimedia data objects. Several potential integration approaches are described as they relate to the DHCP Imaging System.

Computer Communication Networks↗
PubMed1995

Data integrity-conduct of clinical investigations: university investigator perspective.

Clinical investigations are studies designed to evaluate the effectiveness of a new animal drug. Expectations for documentation of events occurring during clinical investigations have been greatly increased. The Food and Drug Administration (FDA) through its Center for Veterinary Medicine (CVM) division recently issued a guideline to address the responsibilities (under 21 CFR 511.1 and 512[j] of the Federal, Food, Drug and Cosmetic Act) of investigators who conduct clinical investigations of new animal drugs and of monitors of these investigations. The guideline is part of a continuing effort by FDA/CVM to propose data integrity initiatives that will continue to assure the reliability and accuracy of the data upon which decisions to approve new animal drugs are based. In addition to the increased documentation, FDA/CVM intends to make real-time inspection of clinical investigations a routine practice. In response to these changes; those involved with clinical investigations will need to make appropriate adjustments. The purpose of this review is to provide additional notification to clinical investigators of the changes in their responsibilities under the new guideline and to provide an investigator perspective of how these changes might impact research efforts.

Animals↗
PubMed2004

Bayesian analysis, pattern analysis, and data mining in health care.

PURPOSE OF REVIEW: To discuss the current role of data mining and Bayesian methods in biomedicine and heath care, in particular critical care. RECENT FINDINGS: Bayesian networks and other probabilistic graphical models are beginning to emerge as methods for discovering patterns in biomedical data and also as a basis for the representation of the uncertainties underlying clinical decision-making. At the same time, techniques from machine learning are being used to solve biomedical and health-care problems. SUMMARY: With the increasing availability of biomedical and health-care data with a wide range of characteristics there is an increasing need to use methods which allow modeling the uncertainties that come with the problem, are capable of dealing with missing data, allow integrating data from various sources, explicitly indicate statistical dependence and independence, and allow integrating biomedical and clinical background knowledge. These requirements have given rise to an influx of new methods into the field of data analysis in health care, in particular from the fields of machine learning and probabilistic graphical models.

Bayes Theorem↗
PubMed2006

Integration of genomic and metabonomic data in systems biology--are we 'there' yet?

The measurement of genes, proteins and metabolites has gained increasing acceptance as a means by which to study the response of an organism to stimuli, whether they are environmental, genetic, pharmacological, toxicological, etc. Typically referred to as genomics, proteomics, and metabonomics or metabolomics, respectively, these methods as independent entities have undoubtedly provided new biological insight that was not attainable a decade ago. Not surprisingly, scientists continue to push the boundaries to extract knowledge from data, and it is currently recognized that the full realization of these technologies is limited by a lack of tools to enable data integration. Integration of these 'omic datasets, or integromics, is desirable as it links the individual biological elements together to provide a more complete understanding of dynamic biological processes. Accordingly, in addition to developing new data analysis methods to extract further details from each of the high-content datasets individually, effort is also being expended to create or improve statistical methods, databases, annotations and pathway mapping to maximize our learning. There are several recent examples, in both mammalian and non-mammalian systems, in which genes, proteins and/or metabolites have been integrated using either biology- or data-driven strategies. Herein, key findings are reviewed, gaps in our current tools and technologies are identified and illustrated, and perspective is provided on the potential of integromics in biological research.

Acetaminophen↗
PubMed2006

The mouse genome database (MGD): new features facilitating a model system.

The mouse genome database (MGD, http://www.informatics.jax.org/), the international community database for mouse, provides access to extensive integrated data on the genetics, genomics and biology of the laboratory mouse. The mouse is an excellent and unique animal surrogate for studying normal development and disease processes in humans. Thus, MGD's primary goals are to facilitate the use of mouse models for studying human disease and enable the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Core MGD data content includes gene characterization and functions, phenotype and disease model descriptions, DNA and protein sequence data, polymorphisms, gene mapping data and genome coordinates, and comparative gene data focused on mammals. Data are integrated from diverse sources, ranging from major resource centers to individual investigator laboratories and the scientific literature, using a combination of automated processes and expert human curation. MGD collaborates with the bioinformatics community on the development of data and semantic standards, and it incorporates key ontologies into the MGD annotation system, including the Gene Ontology (GO), the Mammalian Phenotype Ontology, and the Anatomical Dictionary for Mouse Development and the Adult Anatomy. MGD is the authoritative source for mouse nomenclature for genes, alleles, and mouse strains, and for GO annotations to mouse genes. MGD provides a unique platform for data mining and hypothesis generation where one can express complex queries simultaneously addressing phenotypic effects, biochemical function and process, sub-cellular location, expression, sequence, polymorphism and mapping data. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the incorporation of single nucleotide polymorphism data and search tools, the addition of PIR gene superfamily classifications, phenotype data for NIH-acquired knockout mice, images for mouse phenotypic genotypes, new functional graph displays of GO annotations, and new orthology displays including sequence information and graphic displays.

Animals↗
PubMed2003

Evaluation of common gene expression patterns in the rat nervous system.

In the postgenomic era, integrating data obtained from array technologies (e.g., oligonucleotide microarrays) with published information on eukaryotic genomes is beginning to yield biomarkers and therapeutic targets that are key for the diagnosis and treatment of disease. Nevertheless, identifying and validating these drug targets has not been a trivial task. Although a plethora of bioinformatics tools and databases are available, major bottlenecks for this approach reside in the interpretation of vast amounts of data, its integration into biologically representative models, and ultimately the identification of pathophysiologically and therapeutically useful information. In the field of neuroscience, accomplishing these goals has been particularly challenging because of the complex nature of nerve tissue, the relatively small adaptive nature of induced-gene expression changes, as well as the polygenic etiology of most neuropsychiatric diseases. This report combines published data sets from multiple transcript profiling studies that used GeneChip microarrays to illustrate a postanalysis approach for the interpretation of data from neuroscience microarray studies. By defining common gene expression patterns triggered by diverse events (administration of psychoactive drugs and trauma) in different nerve tissues (telencephalic brain areas and spinal cord), we broaden the conclusions derived from each of the original studies. In addition, the evaluation of the identified overlapping gene lists provides a foundation for generating hypotheses relating alterations in specific sets of genes to common physiological processes. Our approach demonstrates the significance of interpreting transcript profiling data within the context of common pathways and mechanisms rather than specific to a given tissue or stimulus. We also highlight the use of gene expression patterns in predictive biology (e.g., in toxicogenomics) as well as the utility of combining data derived from multiple microarray studies that examine diverse biological events for a broader interpretation of data from a particular microarray study.

Animals↗
PubMed2026

AI-Based 3D Heterogeneous Network Model for Functional Prediction of Epigenetics.

Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.

Epigenesis, Genetic↗
PubMed2005

The use of high-fidelity human patient simulation as an evaluative tool in the development of clinical research protocols and procedures.

Errors in clinical research can be costly, in terms of patient safety, data integrity, and data collection. Data inaccuracy in early subjects of a clinical study may be associated with problems in the design of the protocol, procedures, and data collection tools. High-fidelity patient simulation centers provide an ideal environment to apply human-centered design to clinical trial development. A draft of a complex clinical protocol was designed, evaluated and modified using a high-fidelity human patient simulator in the Duke University Human Simulation and Patient Safety Center. The process included walk-throughs, detailed modifications of the protocol and development of procedural aids. Training of monitors and coordinators provided an opportunity for observation of performance that was used to identify further improvements to the protocol. Evaluative steps were used to design the research protocol and procedures. Iterative modifications were made to the protocol and data collection tools. The success in use of human simulation in the preparation of a complex clinical drug trial suggests the benefits of human patient simulation extend beyond training and medical equipment evaluation. Human patient simulation can provide a context for informal expert evaluation of clinical protocol design and for formal "rehearsal" to evaluate the efficacy of procedures and support tools.

Clinical Competence↗