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PubMed2006

[The database and its data sharing of neuropathic images].

This paper introduces the basic principle and method of establishing a database for neuropathic images, and discusses its significance and data sharing. The database is composed of three data volumes about basic knowledge for images, cranial sectional anatomy and neuropathic images. The data sharing is achieved by using a method of linking the dynamic network of neuropathic images with database of neuropathic images. There are three searching approaches: catalog searching, key words searching and code searching.

Databases, Bibliographic↗
PubMed2025

Beacon Reconstruction Attack: Reconstruction of genomes in genomic data-sharing beacons using summary statistics.

MOTIVATION: Genomic data-sharing beacon protocol, developed by the Global Alliance for Genomics and Health, offers a privacy-preserving mechanism for querying genomic datasets while restricting direct data access. Despite their design, beacons remain vulnerable to privacy attacks. This study introduces a novel privacy vulnerability of the protocol: one can reconstruct large portions of the genomes of all beacon participants by only using the summary statistics reported by the protocol. RESULTS: We introduce a novel optimization-based algorithm that leverages beacon responses and SNP correlations for reconstruction. By optimizing for the SNP correlations and allele frequencies, the proposed approach achieves genome reconstruction with a substantially higher F1-score (70%) compared to baseline methods (45%) on beacons generated using individuals from the HapMap and OpenSNP datasets. We show that reconstructed genomes can be used by downstream applications such as in membership inference attacks against other beacons. Our findings reveal that beacons releasing allele frequencies substantially increase the reconstruction risk, underscoring the need for enhanced privacy-preserving mechanisms to protect genomic data. AVAILABILITY AND IMPLEMENTATION: Our implementation is available at https://github.com/ASAP-Bilkent/Beacon-Reconstruction-Attack.

Genomics↗
PubMed2005

Enhancing data sharing in collaborative research projects with DASH.

We describe a software framework, called DASH, that enables the facile access, maintenance, curation and sharing of computational biology data among collaborating research scientists. The DASH event-based framework enables members of team-based research projects to describe the multistep computational processing pipelines frequently required to generate data for sharing, monitors multiple distributed data stores for changes, and will then automatically invoke the appropriate processing pipeline(s). These pipelines can be used to communicate the results of data analyses to collaborators using mechanisms such as Web Services. We describe the overall design of the DASH system and the application of a simple DASH prototype to a collaborative pharmacogenomics research project involving several dozen researchers located at several different sites--the UCSF Pharmacogenetics of Membrane Transporters project.

Automation↗
PubMed2006

Information models for data sharing.

How to share/exchange data among different databases is still a critical issue. In the medical domain, there is an absolute need for national/international agreement on standard ontologies, terminologies and data models. A global solution is far from being achieved, but in the meantime there are a number of sub-optimal solutions that can be adopted. In this paper two of them, which have already provided, or are close to providing, practical implementations will be described.

Computer Communication Networks↗
PubMed2003

Axiope tools for data management and data sharing.

Many areas of biological research generate large volumes of very diverse data. Managing this data can be a difficult and time-consuming process, particularly in an academic environment where there are very limited resources for IT support staff such as database administrators. The most economical and efficient solutions are those that enable scientists with minimal IT expertise to control and operate their own desktop systems. Axiope provides one such solution, Catalyzer, which acts as flexible cataloging system for creating structured records describing digital resources. The user is able specify both the content and structure of the information included in the catalog. Information and resources can be shared by a variety of means, including automatically generated sets of web pages. Federation and integration of this information, where needed, is handled by Axiope's Mercat server. Where there is a need for standardization or compatibility of the structures usedby different researchers this canbe achieved later by applying user-defined mappings in Mercat. In this way, large-scale data sharing can be achieved without imposing unnecessary constraints or interfering with the way in which individual scientists choose to record and catalog their work. We summarize the key technical issues involved in scientific data management and data sharing, describe the main features and functionality of Axiope Catalyzer and Axiope Mercat, and discuss future directions and requirements for an information infrastructure to support large-scale data sharing and scientific collaboration.

Animals↗
PubMed2003

Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging.

Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.

Algorithms↗
PubMed2000

Clustering methods applied to allele sharing data.

Here we focus on using clustering methods to disentangle the interacting factors that lead to the presentation of complex diseases. Relative pairs are placed in discrete subgroups, or classes, based upon their pattern of allele sharing at a sequence of markers and on concomitant risk factors. The relationship between the locus information and the affectation status of the relative pairs within each subgroup then can be assessed. Cluster analysis (CLA) and latent class analysis (LCA) were applied to sibling allele sharing data from GAW11 simulated data, and to an existing Alzheimer's disease (AD) dataset. Both methods were able to identify markers linked to all 3 disease loci in the GAW11 data. LCA and CLA also replicated regions of chromosomes identified in an analysis of the AD data using affected-sib-pair methods. These analyses indicate that classification tools may be useful for detecting susceptibility genes for complex traits.

Aged↗
PubMed2005

Geographical Information Systems and on-line GIServices for health data sharing and management.

Integrating Geographical Information Systems (GIS) technology and public health experience may represent a solution for a better comprehension of spatial and temporal trends of phenomena. Useful applications can be built that support practitioners in their daily tasks, from risk assessment to prevention programmes. Also, making available data on the Internet through GIServices represents an important goal. Institutions and public health practitioners may benefit from the technological integration of GIS, the Web, handheld and mobile global positioning systems (GPS) devices. Expert users may be supported in deriving thematic maps which represent a spatial synthesis documentation starting from an analytic study expressed in terms of numbers and features. In this paper we show an example of an on-line data sharing and processing application, emphasizing ways GIS can provide added value to health research and management.

Cluster Analysis↗
PubMed2006

Data sharing - a case of shared databases and community use of on-line GIS support systems.

Data management is becoming increasingly simple and complex at the same time. The challenge is to effectively use the increasing number of tools available to manage increasing amounts of environmental information for purposes of data capture, analysis, display, sharing and storage. Government is no longer the main collector and provider of data. Community groups possess vast amounts of data collected through daily work of monitoring the environment in their local community. The chief concerns are data access, sharing, integrity and comparability. The capacity of groups to sustain data management is the key to making the sharing possible. The Southeast Environmental Association has been working with Environment Canada to develop a community, on-line database that will be linked to other geo-spatial data sets to allow instant access to geo-referenced data.

Canada↗
PubMed2026

Underrepresented voices in a Colorado Biobank: Perspectives from focus groups on motivations, return of results, and data sharing.

Most participants in large cohorts, such as biobanks, are of European descent. This lack of representation has been an ongoing challenge in genomic research. Understanding the perspectives on genomics research and participation in biobanks of historically underrepresented populations could provide insight into ways to better engage with these groups. We conducted a series of virtual and in-person focus groups with individuals who self-identified as American Indian or Alaska Native (AI/AN), African American/Black (AA/B), or Hispanic/Latino (H/L) and who were enrolled in the Colorado Center for Personalized Medicine (CCPM) biobank. The focus group discussions were centered on participant experiences, including but not limited to their motivations, return of results, and data sharing. There was a total of 23 participants across the six focus groups. The majority of participants identified as AI/AN (60.9%), followed by H/L (39.1%), and AA/B (21.7%); many participants identified with multiple race/ethnicities. The motivations for participating in the biobank included the potential to advance science and health, the potential for return of results, to learn more about one's ancestry, and a few indicated that they were interested in helping the biobank be more representative of all populations. Notably, many expressed positive feedback of the focus groups and felt that their views were valued, illustrating the importance of community-centered work. Our findings can be used to guide recruitment and engagement of biobank participants, especially from diverse backgrounds, contributing to enhanced partnerships advancing knowledge and healthcare.

biobank↗
PubMedDate not supplied

Instrument monitoring, data sharing, and archiving using Common Instrument Middleware Architecture (CIMA).

The Common Instrument Middleware Architecture (CIMA) aims at Grid-enabling a wide range of scientific instruments and sensors to enable easy access to and sharing and storage of data produced by these instruments and sensors. This paper describes the implementation of CIMA applied to the field of single-crystal X-ray crystallography. To allow the researchers to easily view the current and past data streams from the instruments or sensors in a laboratory, a crystallography portal and associated portlets were developed for this application. The CIMA-based crystallography system provides an opportunity for anyone with Web access to observe and use crystallographic and other data from laboratories that previously had only limited access.

Journal Article↗
PubMed2003

Combined MR data acquisition of multicontrast images using variable acquisition parameters and K-space data sharing.

A new technique to reduce clinical magnetic resonance imaging (MRI) scan time by varying acquisition parameters and sharing k-space data between images, is proposed. To improve data utilization, acquisition of multiple images of different contrast is combined into a single scan, with variable acquisition parameters including repetition time (TR), echo time (TE), and echo train length (ETL). This approach is thus referred to as a "combo acquisition." As a proof of concept, simulations of MRI experiments using spin echo (SE) and fast SE (FSE) sequences were performed based on Bloch equations. Predicted scan time reductions of 25%-50% were achieved for 2-contrast and 3-contrast combo acquisitions. Artifacts caused by nonuniform k-space data weighting were suppressed through semi-empirical optimization of parameter variation schemes and the phase encoding order. Optimization was assessed by minimizing three quantitative criteria: energy of the "residue point spread function (PSF)," energy of "residue profiles" across sharp tissue boundaries, and energy of "residue images." In addition, results were further evaluated by quantitatively analyzing the preservation of contrast, the PSF, and the signal-to-noise ratio. Finally, conspicuity of lesions was investigated for combo acquisitions in comparison with standard scans. Implications and challenges for the practical use of combo acquisitions are discussed.

Algorithms↗
PubMed2026

NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.

MOTIVATION: Biomedical models improve when trained on data pooled across institutions, but sensitive patient records (e.g. genomics, clinical data, and medical images) are difficult to share due to privacy constraints. Moreover, data collected at different sites often have shifted distributions because of covariate differences (including batch effects), so privacy-preserving sharing alone cannot simply resolve cross-site mismatch. Methods that protect individuals while explicitly aligning distributions are needed to enable reliable multi-institutional analyses. RESULTS: We present NoisyFlow, a three-stage differentially private framework for cross-institutional harmonization under distribution shift. In stage I, each site learns a differentially private flow-based generator of its local labeled distribution. In stage II, it learns a neural optimal transport map to a shared reference distribution. In stage III, a central server composes the released models to generate reference-aligned pseudo-data for downstream analysis without accessing raw records. Across four biomedical settings spanning single-cell genomics, histopathology, neurogenomics, and wearable sensing, NoisyFlow reduces distribution shift while preserving downstream utility under formal differential privacy guarantees. AVAILABILITY AND IMPLEMENTATION: The implementation of NoisyFlow is available at https://github.com/gersteinlab/NoisyFlow.

Information Dissemination↗
PubMed2005

Sharing patient data: competing demands of privacy, trust and research in primary care.

BACKGROUND: Patient privacy may conflict with the advancement of knowledge through data sharing. The data contained in primary care records are uniquely comprehensive. AIM: To explore the knowledge and attitudes of patients and members of the primary healthcare team regarding the sharing of data held in primary care records, with particular reference to data sharing for research and the impact that this may have on trust between patients and health professionals. DESIGN OF STUDY: Qualitative study using quota sampled, semi-structured interviews. SETTING: Five general practices in Leicestershire, UK. METHOD: Grounded theory and framework methodology were used. Interviews were transcribed and analysed thematically. RESULTS: Twenty patients and 15 healthcare professionals and managers were interviewed. Patients had limited knowledge of the type of information held in their general practice records and the ways in which these data are shared, but appeared ready to form preliminary views on issues such as data sharing for audit and disease registration. In this climate of limited awareness, there was no suggestion that concern about data sharing for research adversely affects patient trust or leads patients to withhold relevant information from health professionals in primary care. Interviews carried out with staff suggested a lack of clear practice policies regarding data sharing. CONCLUSIONS: General practices may need to develop policies on data sharing, bring these to the attention of their patient population and improve patient awareness about the nature of the data contained in their records. Researchers should ensure that patients are adequately informed about the nature of data contained in patient records when seeking consent for data extraction.

Confidentiality↗
PubMed2002

Health care provider quality improvement organization Medicare data-sharing: a diabetes quality improvement initiative.

BACKGROUND: This paper describes a collaborative Medicare claims data linkage and sharing effort between the Baylor Health Care System (BHCS) and Texas Medical Foundation (TMF, the Texas Quality Improvement Organization) designed to assess the effect of three quality improvement interventions on care delivered to elderly patients with diabetes. The randomized controlled trial is being conducted among a network of primary care physician practices owned by BHCS and focuses on measures of care process and outcome. METHODS: Cohort definition and baseline measurement took place between January 1 and December 31, 2000. BHCS administrative data and TMF-supplied Medicare enrollment data were used to define the January 1, 2001 prevalence cohort of Medicare diabetic beneficiaries meeting study inclusion criteria. A total of 22 practices (with 92 physicians and 2,158 patients) were randomized to one of three interventions, each of which involved performance measurement feedback on three claims-based measures of care process. Physician profiles, generated by TMF using Medicare utilization files, were reported to study physicians via academic detailing sessions with a BHCS physician educator. RESULTS: The January 1 - December 31, 2000 baseline Medicare claims for the January 1, 2001 prevalence cohort were provided to HTPN by TMF in October 2001, representing a ten-month lag in the ability of Quality Improvement Organizations to provide Part B data relative to a specific episode of care time frame. Overall baseline rates for the claims-based process measures were: annual HbA1c testing (86.1%), annual eye examination (60.8%), and annual lipid profile (72.5%). As anticipated, medical-record based rates of annual eye examination were significantly underrepresented. Agreement between claims-based and medical record-based measures was very close for annual HbA1c and annual lipid profile. CONCLUSIONS: The use of Medicare claims data, through collaboration with a QIO, can help health care providers overcome a significant barrier associated with quality improvement initiatives. Limitations associated with the use of Medicare claims can impact implementation of intervention strategies, but do not prevent them from being a practical tool for improving care.

Aged↗