@article {868, title = {The caBIG{\textregistered} Life Science Business Architecture Model.}, journal = {Bioinformatics}, volume = {27}, year = {2011}, month = {2011 May 15}, pages = {1429-35}, abstract = {

MOTIVATION: Business Architecture Models (BAMs) describe what a business does, who performs the activities, where and when activities are performed, how activities are accomplished and which data are present. The purpose of a BAM is to provide a common resource for understanding business functions and requirements and to guide software development. The cancer Biomedical Informatics Grid (caBIG{\textregistered}) Life Science BAM (LS BAM) provides a shared understanding of the vocabulary, goals and processes that are common in the business of LS research.

RESULTS: LS BAM 1.1 includes 90 goals and 61 people and groups within Use Case and Activity Unified Modeling Language (UML) Diagrams. Here we report on the model{\textquoteright}s current release, LS BAM 1.1, its utility and usage, and plans for future use and continuing development for future releases. Availability and Implementation: The LS BAM is freely available as UML, PDF and HTML (https://wiki.nci.nih.gov/x/OFNyAQ).

}, keywords = {Biomedical Research, Computational Biology, Computer Systems, National Cancer Institute (U.S.), Neoplasms, Software, United States, Vocabulary, Controlled}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btr141}, author = {Boyd, Lauren Becnel and Hunicke-Smith, Scott P and Stafford, Grace A and Freund, Elaine T and Ehlman, Michele and Chandran, Uma and Dennis, Robert and Fernandez, Anna T and Goldstein, Stephen and Steffen, David and Tycko, Benjamin and Klemm, Juli D} } @article {869, title = {DDN: a caBIG{\textregistered} analytical tool for differential network analysis.}, journal = {Bioinformatics}, volume = {27}, year = {2011}, month = {2011 Apr 1}, pages = {1036-8}, abstract = {

UNLABELLED: Differential dependency network (DDN) is a caBIG{\textregistered} (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biological conditions. Developed under caBIG{\textregistered}{\textquoteright}s In Silico Research Centers of Excellence (ISRCE) Program, DDN enables differential network analysis and provides an alternative way for defining network biomarkers predictive of phenotypes. DDN also serves as a useful systems biology tool for users across biomedical research communities to infer how genetic, epigenetic or environment variables may affect biological networks and clinical phenotypes. Besides the standalone Java application, we have also developed a Cytoscape plug-in, CytoDDN, to integrate network analysis and visualization seamlessly.

AVAILABILITY: The Java and MATLAB source code can be downloaded at the authors{\textquoteright} web site http://www.cbil.ece.vt.edu/software.htm.

}, keywords = {Animals, Computational Biology, Epigenesis, Genetic, Female, Gene Regulatory Networks, Mammary Glands, Animal, Rats, Software, Systems Biology}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btr052}, author = {Zhang, Bai and Tian, Ye and Jin, Lu and Li, Huai and Shih, Ie-Ming and Madhavan, Subha and Clarke, Robert and Hoffman, Eric P and Xuan, Jianhua and Hilakivi-Clarke, Leena and Wang, Yue} } @article {871, title = {The caBIG annotation and image Markup project.}, journal = {J Digit Imaging}, volume = {23}, year = {2010}, month = {2010 Apr}, pages = {217-25}, abstract = {

Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM.

}, keywords = {Computational Biology, Computer Communication Networks, Databases, Factual, Diagnostic Imaging, Humans, Interdisciplinary Communication, Medical Records Systems, Computerized, National Cancer Institute (U.S.), National Institutes of Health (U.S.), Neoplasms, Program Evaluation, Quality of Health Care, Radiographic Image Enhancement, Radiology Information Systems, Software, Systems Integration, United States, User-Computer Interface}, issn = {1618-727X}, doi = {10.1007/s10278-009-9193-9}, author = {Channin, David S and Mongkolwat, Pattanasak and Kleper, Vladimir and Sepukar, Kastubh and Rubin, Daniel L} } @article {872, title = {Metadata mapping and reuse in caBIG.}, journal = {BMC Bioinformatics}, volume = {10 Suppl 2}, year = {2009}, month = {2009}, pages = {S4}, abstract = {

BACKGROUND: This paper proposes that interoperability across biomedical databases can be improved by utilizing a repository of Common Data Elements (CDEs), UML model class-attributes and simple lexical algorithms to facilitate the building domain models. This is examined in the context of an existing system, the National Cancer Institute (NCI){\textquoteright}s cancer Biomedical Informatics Grid (caBIG). The goal is to demonstrate the deployment of open source tools that can be used to effectively map models and enable the reuse of existing information objects and CDEs in the development of new models for translational research applications. This effort is intended to help developers reuse appropriate CDEs to enable interoperability of their systems when developing within the caBIG framework or other frameworks that use metadata repositories.

RESULTS: The Dice (di-grams) and Dynamic algorithms are compared and both algorithms have similar performance matching UML model class-attributes to CDE class object-property pairs. With algorithms used, the baselines for automatically finding the matches are reasonable for the data models examined. It suggests that automatic mapping of UML models and CDEs is feasible within the caBIG framework and potentially any framework that uses a metadata repository.

CONCLUSION: This work opens up the possibility of using mapping algorithms to reduce cost and time required to map local data models to a reference data model such as those used within caBIG. This effort contributes to facilitating the development of interoperable systems within caBIG as well as other metadata frameworks. Such efforts are critical to address the need to develop systems to handle enormous amounts of diverse data that can be leveraged from new biomedical methodologies.

}, keywords = {Algorithms, Computational Biology, Database Management Systems, Databases, Factual, Medical Informatics, Software, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/1471-2105-10-S2-S4}, author = {Kunz, Isaac and Lin, Ming-Chin and Frey, Lewis} }