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At least 289 records · Page 16Linked to original sources

PubMed2003

ToxoDB: accessing the Toxoplasma gondii genome.

ToxoDB (http://ToxoDB.org) provides a genome resource for the protozoan parasite Toxoplasma gondii. Several sequencing projects devoted to T. gondii have been completed or are in progress: an EST project (http://genome.wustl.edu/est/index.php?toxoplasma=1), a BAC clone end-sequencing project (http://www.sanger.ac.uk/Projects/T_gondii/) and an 8X random shotgun genomic sequencing project (http://www.tigr.org/tdb/e2k1/tga1/). ToxoDB was designed to provide a central point of access for all available T. gondii data, and a variety of data mining tools useful for the analysis of unfinished, un-annotated draft sequence during the early phases of the genome project. In later stages, as more and different types of data become available (microarray, proteomic, SNP, QTL, etc.) the database will provide an integrated data analysis platform facilitating user-defined queries across the different data types.

Animals↗

PubMed1997

Psychobiological models of hippocampal function in learning and memory.

We review current computational models of hippocampal function in learning and memory, concentrating on those that make strongest contact with psychological issues and behavioral data. Some models build upon Marr's early theories for modeling hippocampal field CA3's putative role in the fast, temporary storage of episodic memories. Other models focus on hippocampal involvement in incrementally learned associations, such as classical conditioning. More recent efforts have attempted to bring functional interpretations of the hippocampal region in closer contact with underlying anatomy and physiology. In reviewing these psychobiological models, three major themes emerge. First, computational models provide the conceptual glue to bind together data from multiple levels of analysis. Second, models serve as important tools to integrate data from both animal and human studies. Third, previous psychological models that capture important behavioral principles of memory provide an important top-down constraint for developing computational models of the neural bases of these behaviors.

Animals↗

PubMed1986

Interactive multi-window integration of two-parameter flow cytometric data fields.

Integration is necessary to determine the particle content of regions of interest of flow cytometric two-parameter fields. The improved program of the Cytomic 12 analyzer (1) offers: window trace integration for relatively simple window structures. The field of interest is surrounded by an integration trace (window). Eight independent windows can be stored and successively evaluated. It also offers painted field integration for complicated window structures. The pointer or a small window is interactively moved over the structures to be integrated like the brush of a painter. The "painted field" defines the window to be integrated. Window sets and painted fields can be stored on a floppy disk. Painted fields can be added and may also serve as look up tables for sorting.

Cell Count↗

PubMed2025

An integrative network approach for longitudinal stratification in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms resulting from the loss of dopamine-producing neurons in the brain. Currently, there is no cure for the disease which is in part due to the heterogeneity in patient symptoms, trajectories and manifestations. There is a known genetic component of PD and genomic datasets have helped to uncover some aspects of the disease. Understanding the longitudinal variability of PD is essential as it has been theorised that there are different triggers and underlying disease mechanisms at different points during disease progression. In this paper, we perform longitudinal and cross-sectional experiments to identify which data modalities or combinations of modalities are informative at different time points. We use clinical, genomic, and proteomic data from the Parkinson's Progression Markers Initiative. We validate the importance of flexible data integration by highlighting the varying combinations of data modalities for optimal stratification at different disease stages in idiopathic PD. We show there is a shared signal in the DNAm signatures of participants with a mutation in a causal gene of PD and participants with idiopathic PD. We also show that integration of SNPs and DNAm data modalities has potential for use as an early diagnostic tool for individuals with a genetic cause of PD.

Parkinson Disease↗

PubMed1995

Cognitive computer-based video analysis: its application in assessing the usability of medical systems.

This paper describes a methodology, based on cognitive research, for assessing the usability of medical computing systems. The issue of developing appropriate evaluation tools, both for use in the design process and for analysis of end products, is beginning to be recognized as being of great importance. In this paper, the use of video recording for collecting empirical data on system usability is detailed. The techniques described allow for the collection of an integrated data set consisting of the transcripts of physicians as they "think aloud" in interacting with a medical system, along with video records of user-computer interaction. The use of coding methodologies and a computer-based annotation system for the analysis of video data are described. Our preliminary experience indicates that this methodology offers a powerful way for assessing physicians' informational needs. Implications for the development and evaluation of medical information systems are discussed.

Computer Systems↗

PubMed2002

Analytical reproducibility in (1)H NMR-based metabonomic urinalysis.

Metabonomic analysis of biofluids and tissues utilizing high-resolution NMR spectroscopy and chemometric techniques has proven valuable in characterizing the biochemical response to toxicity for many xenobiotics. To assess the analytical reproducibility of metabonomic protocols, sample preparation and NMR data acquisition were performed at two sites (one using a 500 MHz and the other using a 600 MHz system) using two identical (split) sets of urine samples from an 8-day acute study of hydrazine toxicity in the rat. Despite the difference in spectrometer operating frequency, both datasets were extremely similar when analyzed using principal components analysis (PCA) and gave near-identical descriptions of the metabolic responses to hydrazine treatment. The main consistent difference between the datasets was related to the efficiency of water resonance suppression in the spectra. In a 4-PC model of both datasets combined, describing all systematic dose- and time-related variation (88% of the total variation), differences between the two datasets accounted for only 3% of the total modeled variance compared to ca. 15% for normal physiological (pre-dose) variation. Furthermore, <3% of spectra displayed distinct inter-site differences, and these were clearly identified as outliers in their respective dose-group PCA models. No samples produced clear outliers in both datasets, suggesting that the outliers observed did not reflect an unusual sample composition, but rather sporadic differences in sample preparation leading to, for example, very dilute samples. Estimations of the relative concentrations of citrate, hippurate, and taurine were in >95% correlation (r(2)) between sites, with an analytical error comparable to normal physiological variation in concentration (4-8%). The excellent analytical reproducibility and robustness of metabonomic techniques demonstrated here are highly competitive compared to the best proteomic analyses and are in significant contrast to genomic microarray platforms, both of which are complementary techniques for predictive and mechanistic toxicology. These results have implications for the quantitative interpretation of metabonomic data, and the establishment of quality control criteria for both regulatory agencies and for integrating data obtained at different sites.

Animals↗

PubMed1997

Management of Information in Radiation Oncology: An Integrated System for Scheduling, Treatment, Billing, and Verification.

An effective information system is an essential prerequisite to delivering quality patient care at competitive costs. From scheduling and billing to complex treatment machine control and verification, the quality of the information system strongly affects the efficiency and accuracy with which patient care is delivered. The standard paper-based information system used in many clinics suffers form inefficiencies and incompleteness in scheduling and billing, no centralized database and the inability to generate routine reports and communicate with other information systems. Many of these problems are resolved by the introduction of an electronic information system. The implementation, gains, and limitations of two electronic information systems are discussed. While limitations such as the lack of complete seamless integration of all information still exist, major improvements have been made in efficiency, accuracy, data integrity, and reporting and billing completeness.

Journal Article↗

PubMed1998

Issues in identification and linkage of patient records across an integrated delivery system.

Historically, the health information systems community has viewed linking personal records as a mundane task. The oversimplified view that routine database manipulation can accurately identify multiple records for a single individual is erroneous, an assumption based on a misperception of the quality of the underlying data. Such data have been adversely affected by the evolution of individual facility patient indexes from multiple systems and the results of backload procedures, and the lack of focus on the need for data integrity by users of the automated systems. Much of the random, invalid data we identify on a daily basis is directly associated with the need for system users to place data in the patient record while they face the situation of having no obvious data field in which to place them. Combined with an underlying lack of standards for the collection of personal identification information, this results in pure chaos when reviewing an MPI file containing a million records at the start of a linkage evaluation project. We have documented the considerable effort that must therefore be made in standardizing the MPI files using stringent analytical procedures and applying common edit routines before commencing record linkage. This preprocessing effort must then be supplemented with sophisticated matching procedures that can handle the dual challenge of minimizing false negatives (the failure to identify true linkages) and false positives (the incorrect linking of records that do not represent the same person). The identification of pairs of linked records does not, however, complete an EPI loading. Because it is fairly common for a multiple facility linkage evaluation to identify more than two medical record numbers for the same patient, and the primary goal of an EPI is to assign a unique identifier for the patient which will link that patient's multiple files, it becomes necessary to develop a means of readily associating three or more records for the same patient. One approach we have used with great success is to assign a common, sequential identification number to all linked medical record numbers for the same patient regardless of facility. The assignment of linkage identification numbers is computer-intensive and is generally accomplished with a highly iterative process. Both system memory and hard disk resources are fully tested as the number of good linkages in an overlap evaluation reaches the half-million mark or greater. Because the primary linkage analysis goal is to develop linkages on pairs of records, with confidence levels based on the comparison of information for those two records, thresholds must be set to decide which linkages should be accepted as true without any human evaluation. If the threshold is set too low, the defined linkage groups may incorrectly join the medical record numbers for different persons. But if the threshold is set too high, there will be undesired duplication of persons in the enterprise system. As in the identification of the underlying linkage pairs, the development of a confidence measure greatly facilitates the assignment of the unique identification numbers needed in the EPI implementation.

Algorithms↗

PubMedDate not supplied

Implementation of a management information system for hospital use at the Policlinico Universitario A. Gemelli.

First, the experience with the implementation of HISs at the "Policlinico Gemelli", is reported. They were adopted since long for management support. In fact, they are playing an increasingly relevant role in health care facilities. Subsequently, operational applications are described with particular reference to the most recent ones, implemented within European projects with partial EEC financing. They are very useful to the medical and nursing personnel and for patient data management during treatment. Finally, the accounting system for cost centers, using, processing and integrating data of all used information systems, is described. It affords a low cost aggregation of all information about costs and services required for the management of four-hundred elementary operating units which constitute the structure. This system is representative of all managerial information of the "Policlinico" which is included in monthly reports, also described. They are sent to the Direction and managers of the organizational units. The system is a strategic tool of the utmost importance for rapid and effective management.

Hospital Information Systems↗

PubMed1999

IXDB, an X chromosome integrated database (update).

Chromosome specific databases are an important research tool as they integrate data from different directions, such as genetic and physical mapping data, expression data, sequences etc. They supplement the genome-wide repositories in molecular biology, such as GenBank, Swiss-Prot or OMIM, which usually concentrate on one type of information. The Integrated X Chromosome Database (IXDB, http://ixdb.mpimg-berlin-dahlem.mpg.de/) is a repository for physical mapping data of the human X chromosome and aims at providing a global view of genomic data at a chromosomal level. We present here an update of IXDB which includes schema extensions for storing submaps and sequence information, additional links to external databases, and the integration of an increasing number of physical and transcript mapping data. The gene data was completely updated according to the approved gene symbols of the HUGO Nomenclature Committee. IXDB receives over 1000 queries per month, an indication that its content is valuable to researchers seeking mapping data of the human X chromosome.

Chromosome Mapping↗

PubMed2026

Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.

Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.

Humans↗

PubMed1998

The evolution of an integrated timeline for oncology patient healthcare.

The introduction of computers in the medical environment has contributed to the proliferation of medical data, often making it difficult to consolidate information on a single patient. In patients with complex medical problems, such as oncology patients, the lack of data integration can negatively impact on patient care. This paper presents an infrastructure for the creation of an integrated multimedia timeline that automatically combines patient information from distributed hospital information sources, and creates a visual summary of pertinent events in a patient's medical history. In this prototype, we focus on oncology patients under treatment for advanced cancers.

Database Management Systems↗

PubMed1990

Precautions in topographic mapping and in evoked potential map reading.

First, we consider the main points that must be addressed when constructing topographic maps: types of projection, methods of interpolation, number and locations of recording electrodes, and color scales. Data integrity and precautions in map interpretation are then examined for the case of evoked potential data.

Brain↗

PubMedDate not supplied

Cognitive evaluation of decision making processes and assessment of information technology in medicine.

This paper describes cognitive methods for analyzing medical decision making and evaluating medical information systems. The overall approach focuses on understanding the processes involved in the decision making and reasoning of health care workers, both with and without the use of information technologies. The issue of developing appropriate evaluation tools, for use in the design and analysis of medical information systems is considered to be of great importance. However, conventional methods are limited in their ability to identify and characterize the effects of information technology on the cognitive processes involved in decision making and reasoning. In this paper a range of methods are described involving video recording for collecting data on the use of information systems. The techniques described allow for the collection of an integrated data set consisting of transcripts of health care workers as they 'think aloud' in interacting with a medical system, along with complete video records of user-computer interaction. In addition, the methods can be extended to allow for the collection of process data from video recording of systems in actual clinical and emergency situations. The use of a variety of approaches, borrowing from research in cognitive science, is discussed. The development and application of these evaluation methods within the Canadian Centres of Excellence network HEALNet is subsequently described. Finally, implications for the development and evaluation of medical information systems are considered.

Cognition↗

PubMed2001

Physical mapping: integrating computational and molecular genetic data.

A crucial step beyond the identification of genetic linkage of a disease to a chromosomal region is the production of a physical map that will allow the identification of candidate genes. Although the process of physical map building has been facilitated by the flow of data released by the Human Genome Project, gathering all the information together requires significant effort. In a previous study, we reported linkage between Bipolar Affective Disorder and the chromosomal location 4p15.3--p16.1. In this review we use this example to describe how to collect publicly available sequence, DNA fingerprint, and genetic marker data and integrate these with empirical data to build a large scale high resolution physical map of a region. Methods used to identify new genetic markers and candidate genes within a circumscribed region are also presented.

Databases, Factual↗

PubMed1996

Health care informatics: the key to successful disease management.

Health services integration and disease state management (DSM) require improved health care informatics systems. Accurate, comprehensive patient information and an integrated data infrastructure are needed for all stages of DSM, from development and implementation of programs to evaluation and continuous program improvement. The lack of an integrated information infrastructure is one of the leading obstacles to achieving a comprehensive electronic patient data system. This article examines initiatives underway to make the computer-based patient record a reality.

Attitude of Health Personnel↗

PubMed2026

Unlocking the Full Potential of Spatial Omics in Plants: Practical Challenges, Solutions, and a Path Forward.

Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Journal Article↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗