Biomedical subjects

Stephen Langella

Publications and source records attributed to Stephen Langella.

3 recordsLinked to original sources

PubMed2006

caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid.

MOTIVATION: The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems. RESULTS: At the heart of the caBIG approach to federated interoperability effort is a Grid middleware infrastructure, called caGrid. In this paper we describe the caGrid framework and its current implementation, caGrid version 0.5. caGrid is a model-driven and service-oriented architecture that synthesizes and extends a number of technologies to provide a standardized framework for the advertising, discovery, and invocation of data and analytical resources. We expect caGrid to greatly facilitate the launch and ongoing management of coordinated cancer research studies involving multiple institutions, to provide the ability to manage and securely share information and analytic resources, and to spur a new generation of research applications that empower researchers to take a more integrative, trans-domain approach to data mining and analysis. AVAILABILITY: The caGrid version 0.5 release can be downloaded from https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/. The operational test bed Grid can be accessed through the client included in the release, or through the caGrid-browser web application http://cagrid-browser.nci.nih.gov.

Biomarkers, Tumor↗

PubMed2006

An XML-based system for synthesis of data from disparate databases.

Diverse data sets have become key building blocks of translational biomedical research. Data types captured and referenced by sophisticated research studies include high throughput genomic and proteomic data, laboratory data, data from imagery, and outcome data. In this paper, the authors present the application of an XML-based data management system to support integration of data from disparate data sources and large data sets. This system facilitates management of XML schemas and on-demand creation and management of XML databases that conform to these schemas. They illustrate the use of this system in an application for genotype-phenotype correlation analyses. This application implements a method of phenotype-genotype correlation based on phylogenetic optimization of large data sets of mouse SNPs and phenotypic data. The application workflow requires the management and integration of genomic information and phenotypic data from external data repositories and from the results of phenotype-genotype correlation analyses. Our implementation supports the process of carrying out a complex workflow that includes large-scale phylogenetic tree optimizations and application of Maddison's concentrated changes test to large phylogenetic tree data sets. The data management system also allows collaborators to share data in a uniform way and supports complex queries that target data sets.

Animals↗

PubMed2005

A grid-based image archival and analysis system.

Here the authors present a Grid-aware middleware system, called GridPACS, that enables management and analysis of images in a massive scale, leveraging distributed software components coupled with interconnected computation and storage platforms. The need for this infrastructure is driven by the increasing biomedical role played by complex datasets obtained through a variety of imaging modalities. The GridPACS architecture is designed to support a wide range of biomedical applications encountered in basic and clinical research, which make use of large collections of images. Imaging data yield a wealth of metabolic and anatomic information from macroscopic (e.g., radiology) to microscopic (e.g., digitized slides) scale. Whereas this information can significantly improve understanding of disease pathophysiology as well as the noninvasive diagnosis of disease in patients, the need to process, analyze, and store large amounts of image data presents a great challenge.

Computer Communication Networks↗