Cancer Biomedical Informatics Grid Research Articles

CaTissue Suite to OpenSpecimen: Developing an extensible, open source, web-based biobanking management system

The National Cancer Institute (NCI) Cancer Biomedical Informatics Grid® (caBIG®) program established standards and best practices for biorepository data management by creating an infrastructure to propagate biospecimen resource sharing while maintaining data integrity and security. caTissue Suite, a biospecimen data management software tool, has evolved from this effort. More recently, the caTissue Suite continues to evolve as an open source initiative known as OpenSpecimen. The essential functionality of OpenSpecimen includes the capture and representation of highly granular, hierarchically-structured data for biospecimen processing, quality assurance, tracking, and annotation. Ideal for multi-user and multi-site biorepository environments, OpenSpecimen permits role-based access to specific sets of data operations through a user-interface designed to accommodate varying workflows and unique user needs. The software is interoperable, both syntactically and semantically, with an array of other bioinformatics tools given its integration of standard vocabularies thus enabling research involving biospecimens. End-users are encouraged to share their day-to-day experiences in working with the application, thus providing to the community board insight into the needs and limitations which need be addressed. Users are also requested to review and validate new features through group testing environments and mock screens. Through this user interaction, application flexibility and interoperability have been recognized as necessary developmental focuses essential for accommodating diverse adoption scenarios and biobanking workflows to catalyze advances in biomedical research and operations. Given the diversity of biobanking practices and workforce roles, efforts have been made consistently to maintain robust data granularity while aiding user accessibility, data discoverability, and security within and across applications by providing a lower learning curve in using OpenSpecimen. Iterative development and testing cycles provide continuous maintenance and up-to-date capabilities for this freely available, open-access, web-based software application that is globally-adopted at over 25 institutions.

Global data for local science: Assessing the scale of data infrastructures in biological and biomedical research

The use of online databases to collect and disseminate data is typically portrayed as crucial to the management of ‘big science’. At the same time, databases are not deemed successful unless they facilitate the re-use of data towards new scientific discoveries, which often involves engaging with several highly diverse and inherently unstable research communities. This article examines the tensions encountered by database developers in their efforts to foster both the global circulation and the local adoption of data. I focus on two prominent attempts to build data infrastructures in the fields of plant science and cancer research over the last decade: The Arabidopsis Information Resource and the Cancer Biomedical Informatics Grid. I show how curators’ experience of the diverse and dynamic nature of biological research led them to envision databases as catering primarily for local, rather than global, science; and to structure them as platforms where methodological and epistemic diversity can be expressed and explored, rather than denied or overcome. I conclude that one way to define the scale of data infrastructure is to consider the range and scope of the biological and biomedical questions that it helps to address; and that within this perspective, databases have a larger scale than the science that they serve, which tends to remain fragmented into a wide variety of specialised projects.

The integrated proactive surveillance system for prostate cancer.

In this paper, we present the design and implementation of the integrated proactive surveillance system for prostate cancer (PASS-PC). The integrated PASS-PC is a multi-institutional web-based system aimed at collecting a variety of data on prostate cancer patients in a standardized and efficient way. The integrated PASS-PC was commissioned by the Prostate Cancer Foundation (PCF) and built through the joint of efforts by a group of experts in medical oncology, genetics, pathology, nutrition, and cancer research informatics. Their main goal is facilitating the efficient and uniform collection of critical demographic, lifestyle, nutritional, dietary and clinical information to be used in developing new strategies in diagnosing, preventing and treating prostate cancer.The integrated PASS-PC is designed based on common industry standards – a three tiered architecture and a Service- Oriented Architecture (SOA). It utilizes open source software and programming languages such as HTML, PHP, CSS, JQuery, Drupal and MySQL. We also use a commercial database management system – Oracle 11g. The integrated PASS-PC project uses a “confederation model” that encourages participation of any interested center, irrespective of its size or location. The integrated PASS-PC utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The integrated PASS-PC controlled vocabulary is harmonized with the National Cancer Institute (NCI) Thesaurus. Currently, two cancer centers in the USA are participating in the integrated PASS-PC project.The final system has three main components: 1. National Prostate Surveillance Network (NPSN) website; 2. NPSN myConnect portal; 3. Proactive Surveillance System for Prostate Cancer (PASS-PC). PASS-PC is a cancer Biomedical Informatics Grid (caBIG) compatible product. The integrated PASS-PC provides a foundation for collaborative prostate cancer research. It has been built to meet the short term goal of gathering prostate cancer related data, but also with the prerequisites in place for future evolution into a cancer research informatics platform. In the future this will be vital for successful prostate cancer studies, care and treatment.

A framework for assessing clinical trial informatics systems: case of caBIG and its clinical trial management system

Clinical trials are big business, incorporating information from many participants and situations and creating challenges in the acquisition, storage, and sharing of information amongst healthcare researchers. As institutions consider which clinical trial informatics system to choose for their capital investment, it will be important to identify an approach for choosing a satisfactory system. This paper outlines a proactive approach in helping institutions in selecting a shift to an ideal clinical trial informatics system. This approach is organised under the new acronym P3IT, consisting of product, price, people, industry context, and technology trends. This includes internal analysis to explore product attributes and price, and human resource capacity (people), as well as external analysis to explore a Porter's five forces assessment of competitor rivalry; and technology and trends (external analysis to consider future technology and trends in clinical trial informatics systems and the case study product). Using this approach, this paper then explores a case study analysis of the Cancer Biomedical Informatics Grid (caBIG), developed by the National Cancer Institute, and its Clinical Trial Management System (CTMS) workspace which uses Clinical Trials Suite software.

The caBIG® Life Science Business Architecture Model.

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®) 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'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).Contact: lbboyd@bcm.edu; laurenbboyd@gmail.comSupplementary information: Supplementary data) are avaliable at Bioinformatics online.

Informatics in Radiology: Automated Structured Reporting of Imaging Findings Using the AIM Standard and XML

Quantitative and descriptive imaging data are a vital component of the radiology report and are frequently of paramount importance to the ordering physician. Unfortunately, current methods of recording these data in the report are both inefficient and error prone. In addition, the free-text, unstructured format of a radiology report makes aggregate analysis of data from multiple reports difficult or even impossible without manual intervention. A structured reporting work flow has been developed that allows quantitative data created at an advanced imaging workstation to be seamlessly integrated into the radiology report with minimal radiologist intervention. As an intermediary step between the workstation and the reporting software, quantitative and descriptive data are converted into an extensible markup language (XML) file in a standardized format specified by the Annotation and Image Markup (AIM) project of the National Institutes of Health Cancer Biomedical Informatics Grid. The AIM standard was created to allow image annotation data to be stored in a uniform machine-readable format. These XML files containing imaging data can also be stored on a local database for data mining and analysis. This structured work flow solution has the potential to improve radiologist efficiency, reduce errors, and facilitate storage of quantitative and descriptive imaging data for research.

DDN: a caBIG® analytical tool for differential network analysis

Differential dependency network (DDN) is a caBIG® (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biological conditions. Developed under caBIG®'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. The Java and MATLAB source code can be downloaded at the authors' web site http://www.cbil.ece.vt.edu/software.htm.

PCCR: Pancreatic Cancer Collaborative Registry

The Pancreatic Cancer Collaborative Registry (PCCR) is a multi-institutional web-based system aimed to collect a variety of data on pancreatic cancer patients and high-risk subjects in a standard and efficient way. The PCCR was initiated by a group of experts in medical oncology, gastroenterology, genetics, pathology, epidemiology, nutrition, and computer science with the goal of facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention and treatment strategies against pancreatic cancer. The PCCR is a multi-tier web application that utilizes Java/JSP technology and has Oracle 10 g database as a back-end. The PCCR uses a “confederation model” that encourages participation of any interested center, irrespective of its size or location. The PCCR utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The PCCR controlled vocabulary is harmonized with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). The PCCR questionnaire has accommodated standards accepted in cancer research and healthcare. Currently, seven cancer centers in the USA, as well as one center in Italy are participating in the PCCR. At present, the PCCR database contains data on more than 2,700 subjects (PC patients and individuals at high risk of getting this disease). The PCCR has been certified by the NCI Center for Biomedical Informatics and Information Technology as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product. The PCCR provides a foundation for collaborative PC research. It has all the necessary prerequisites for subsequent evolution of the developed infrastructure from simply gathering PC-related data into a biomedical computing platform vital for successful PC studies, care and treatment. Studies utilizing data collected in the PCCR may engender new approaches to disease prognosis, risk factor assessment, and therapeutic interventions.

Multicenter Breast Cancer Collaborative Registry

The Breast Cancer Collaborative Registry (BCCR) is a multicenter web-based system that efficiently collects and manages a variety of data on breast cancer (BC) patients and BC survivors. This registry is designed as a multi-tier web application that utilizes Java Servlet/JSP technology and has an Oracle 11g database as a back-end. The BCCR questionnaire has accommodated standards accepted in breast cancer research and healthcare. By harmonizing the controlled vocabulary with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the BCCR provides a standardized approach to data collection and reporting. The BCCR has been recently certified by the National Cancer Institute’s Center for Biomedical Informatics and Information Technology (NCI CBIIT) as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product.The BCCR is aimed at facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention, treatment, and survivorship strategies against breast cancer. Currently, seven cancer institutions are participating in the BCCR that contains data on almost 900 subjects (BC patients and survivors, as well as individuals at high risk of getting BC).

PUGSVM: a caBIGTM analytical tool for multiclass gene selection and predictive classification

Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases. http://www.cbil.ece.vt.edu/caBIG-PUGSVM.htm.

Semantic web: key technologies for bridging imaging and translational medicine

Imaging in translational medicine Translational medicine has emerged as a new concept in medicine resulting from closer interactions between the research realm and clinical practice, as suggested by the expression ‘from bench to bedside’. The essence of translational medicine is that it not only leads to more efficient health care delivery (e.g., new drugs and procedures), but also to a more in-depth understanding of underlying biological mechanisms [1,2]. The term ‘research realm’ here means not only preclinical and clinical research (e.g., clinical trials) but something broader, embracing both basic research and translational research aiming to put biological facts obtained in experimental works (in animals) in perspective with other observations made in man concerning different pathologies, and involving common physiological and biological systems. This concept has gained credibility thanks to recent initiatives such as the Cancer Biomedical Informatics Grid (CaBIG), an initiative led by the National Cancer Institute in the USA that aims to transform the way cancer research is performed by better connecting the entire cancer community [101]. In particular, this initiative emphasizes the major contribution that biomedical informatics infrastructures can make towards this goal. Imaging plays a major role in modern medicine owing to the continuous progress of imaging equipment during the last 30 years, and this role will certainly be even more prominent in future translational medicine. A salient characteristic of modern imaging is that it is becoming more and more quantitative [3]. This means it no longer solely aims to deliver images to be interpreted by a radiologist, but also to deliver ‘measurements’ of well-defined parameters. Such parameters may be derived from acquired images and reflect some physical characteristic of tissues (e.g., the T 1 relaxation time in MRI, or the concentration of a tracer in molecular imaging). It may also result from a complex analysis of images using texture analysis or segmentation, and denote some structural, physiological or functional index. The latter are often called ‘imaging biomarkers’, an analogy with other kinds of biological biomarkers [4,5]. This notion of measurement is important and refers to a complete methodology involving quality assurance based on calibration, to guarantee reproducibility in time and space, which puts heavy constraints on image acquisition and image processing protocols [6]. It also requires a clear, explicit and consensual definition of the quantities being measured; this is a key feature with respect to relevant reuse of information in the context of translational medicine. Indeed, it is not sufficient that such numeric values be communicated to research data repositories (thanks to picture archiving and communication systems), one must also ensure that the precise semantics of this data be shared as well. This is a necessary condition for successful sharing and relevant correlation of this information with other data available from other sources.

CaNanoLab – A Tool To Benefit Biomedical Nanomaterials Research

AbstractThe cancer Nanotechnology Laboratory Web portal ("http://cananolab.nci.nih.gov/caNanoLab/welcome.do":http://cananolab.nci.nih.gov/caNanoLab/welcome.do) is designed to promote sharing of nanomedicine data and to provide support for the annotation of nanoparticles with information about their composition, function, and characterization. The project is a part of NCI cancer Biomedical Informatics Grid (caBIG®), and includes a collaboration among the NCI Center for Biomedical Informatics and Information Technology (CBIIT), the NCI Nanotechnology Characterization Laboratory (NCL), and the NCI Alliance for Nanotechnology.Currently, caNanolab contains nanomaterial data of 765 "public" samples. 18 samples were contributed by National Characterization Lab (USA), while the remaining 747 were submitted by a curator at Washington University in St. Louis from 114 publications. The curated samples include simple nanoparticles, such as emulsions, metal oxides, nanohorns, nanorods, polymers, quantum dots, as well as complex particles, such as dendriworms, which are conjugates of crosslinked dextran-coated iron oxide particles and dendrimers. Data submission involves curation of sample composing elements, their function (imaging, targeting, therapeutic), physico-chemical (size, molecular weight, surface/zeta potential, etc.) properties, and in vitro (cytotoxicity, blood contact,targeting/gene expression, etc.) characterizations, based on information provided in corresponding publications or obtained from the authors.caNanoLab also contributes to and utilizes resources of NanoParticle Ontology("http://www.nano-ontology.org":http://www.nano-ontology.org).

CaGrid Workflow Toolkit: A taverna based workflow tool for cancer grid

BackgroundIn biological and medical domain, the use of web services made the data and computation functionality accessible in a unified manner, which helped automate the data pipeline that was previously performed manually. Workflow technology is widely used in the orchestration of multiple services to facilitate in-silico research. Cancer Biomedical Informatics Grid (caBIG) is an information network enabling the sharing of cancer research related resources and caGrid is its underlying service-based computation infrastructure. CaBIG requires that services are composed and orchestrated in a given sequence to realize data pipelines, which are often called scientific workflows.ResultsCaGrid selected Taverna as its workflow execution system of choice due to its integration with web service technology and support for a wide range of web services, plug-in architecture to cater for easy integration of third party extensions, etc. The caGrid Workflow Toolkit (or the toolkit for short), an extension to the Taverna workflow system, is designed and implemented to ease building and running caGrid workflows. It provides users with support for various phases in using workflows: service discovery, composition and orchestration, data access, and secure service invocation, which have been identified by the caGrid community as challenging in a multi-institutional and cross-discipline domain.ConclusionsBy extending the Taverna Workbench, caGrid Workflow Toolkit provided a comprehensive solution to compose and coordinate services in caGrid, which would otherwise remain isolated and disconnected from each other. Using it users can access more than 140 services and are offered with a rich set of features including discovery of data and analytical services, query and transfer of data, security protections for service invocations, state management in service interactions, and sharing of workflows, experiences and best practices. The proposed solution is general enough to be applicable and reusable within other service-computing infrastructures that leverage similar technology stack.

Informatics in Radiology: An Information Model of the DICOM Standard

The Digital Imaging and Communications in Medicine (DICOM) Standard is a key foundational technology for radiology. However, its complexity creates challenges for information system developers because the current DICOM specification requires human interpretation and is subject to nonstandard implementation. To address this problem, a formally sound and computationally accessible information model of the DICOM Standard was created. The DICOM Standard was modeled as an ontology, a machine-accessible and human-interpretable representation that may be viewed and manipulated by information-modeling tools. The DICOM Ontology includes a real-world model and a DICOM entity model. The real-world model describes patients, studies, images, and other features of medical imaging. The DICOM entity model describes connections between real-world entities and the classes that model the corresponding DICOM information entities. The DICOM Ontology was created to support the Cancer Biomedical Informatics Grid (caBIG) initiative, and it may be extended to encompass the entire DICOM Standard and serve as a foundation of medical imaging systems for research and patient care.

Using biomedical informatics to see the forest AND the trees for molecular diagnostics in cancer therapeutic development

Abstract Cancer research in general—and molecular diagnostic development in particular—are leveraging the diverse collection of publicly-accessible molecular information that underpins disease. The ability to access and integrate this information will be crucial to the development of molecular diagnostics. Use of this data requires overcoming the silos and data disconnects common throughout biomedicine and new models for interpreting this massive amount of information. The National Cancer Institute has developed the caBIG® initiative (Cancer Biomedical Informatics Grid) to integrate the diverse multidimensional data. An active area of research is developing biologic process models for integrating and interpreting this massive volume of data. The Cancer Genome Atlas (TCGA) is an example of the challenges faced in transforming data to information. Large volumes of diverse, multidimensional data including various genomic characterizations and clinical information must be synthesized. Through the caBIG® generated Cancer Molecular Analysis portal (http://cma.nci.nih.gov), it is possible to see integrated views of the TCGA data from different research perspectives including genomic and clinical views. Tools leveraging this data can be used to display and analyze the data in biologic networks. As more TCGA data and additional molecular data are shared through the portal it will become increasingly possible to obtain a comprehensive view of the molecular basis of cancer and drive development of molecular diagnostics.

Technology's role in healthcare reform

Technology's role in healthcare reform

Publication and consumption of caBIG data services using .NET

AbstractThe cancer Biomedical Informatics Grid (caBIG) is revolutionizing the way medical researchers share information and collaborate. A key to caBIG's continued success will be interoperability. However, to date, only a single code base (in Java) has been used to create a set of tools and run‐time services for caBIG. This paper presents the first significant exploration into the use of Microsoft's .NET Framework and Visual Studio for caBIG. Given its substantial existing community, a .NET‐based set of tools for caBIG can significantly increase the pool of qualified software designers and developers for caBIG. Arguably more importantly, a second development foundation could facilitate revisiting a broad set of design decisions made to date in caBIG that have perhaps been unduly based directly or indirectly on a single underlying software technology. We begin by describing issues we have encountered in building relatively simple .NET‐based clients to existing caBIG services. Next, we describe how we leverage Microsoft ADO.NET Data Services as the foundation for caBIG Data Services, in particular for the caBIO data set. We find that ADO.NET Data Services has a uniquely strong potential to facilitate rapid development and deployment. We conclude with a discussion of the roadmap of our project's future activities. Copyright © 2010 John Wiley & Sons, Ltd.

ProteomeCommons.org Collaborative Annotation and Project Management Resource Integrated With the Tranche Repository

ProteomeCommons.org has implemented a resource that incorporates concepts of Web 2.0 social networking for collaborative annotation of data sets placed in the Tranche repository. The annotation tools are part of a project management resource that is effective for individual laboratories or large distributed groups. The creation of the resource was motivated by the need for a way to encourage annotation of data sets with high accuracy and compliance rates. The system is designed to respond to the dynamic nature of research in an easy-to-use fashion through the use of a dynamic data model that does not inhibit the innovation that is important for basic research. Placing the annotation tool within a project manager allows annotation to occur over the life of the project and provides the security and monitoring capabilities needed for large or small collaborative projects. The resource effectively supports distributed groups of investigators working on common data sets and is available immediately at https://ProteomeCommons.org . In addition, a silver compliant data resource based on ProteomeCommons.org has been developed for cancer Biomedical Informatics Grid (caBIG) to allow much broader access to the annotations describing data sets in the Tranche repository.

Abstract 99: The caBIG® life sciences distribution: Integrative tools to support translational research

Abstract The cancer Biomedical Informatics Grid® (caBIG®) is a collaborative network designed to accelerate the translation of discoveries from research to clinical care. This extensible informatics platform integrates diverse data types and supports interoperable software tools in the areas of clinical sciences, biospecimen management, imaging, and basic research. The Life Sciences Distribution (LSD) product bundle brings together a range of tools that support translational research, all connected via grid network technology. Tools included in the bundle support management and annotation of microarray data (caArray), biospecimens (caTissue Suite), imaging data (NBIA), clinical data (CTODS) and genome-wide association studies (caGWAS). LSD also includes integrative tools that allow scientist to search data across different data repositories connected to the grid, and analyze and integrate these data across different data types. geWorkbench supports integrative analysis and visualization of expression data, sequences, pathways and protein structures. cancer Bench-to-Bedside (caB2B) allows end-users to search biospecimens, images and array data across the grid and makes it possible to execute queries such as: “Are there any gene expression microarray data available from patients with Stage III lung cancer and are there corresponding in vivo images available for the affected patients?” Such a query would potentially span information federated across several instances of caArray, caTissue, and NBIA around the world. caIntegrator2 allows researchers to set up study specific custom web portals that bring together heterogeneous clinical, microarray and in vivo imaging data. This tool provides a graphical user interface to allow a study author to “point” to data of interest in systems on the grid and to then bring that data (or pointers to it, in the case of images) into the data warehouse. Once this information is in the caIntegrator2 environment, end user scientists can then run advanced queries, perform correlative outcomes analysis using Kaplan-Meier survival plots, and access analysis and visualization tools on and off the grid. All tools in the LSD are open source and community members are encouraged to participate and contribute. More information on the LSD suite of products, including installation packages, user and installation guides, and links to exemplar installations can be found at https://cabig.nci.nih.gov/adopt/LSD/ Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 99.

Biomedical informatics and outcomes research: enabling knowledge-driven health care.

At its core, the practice of medicine is an information-intensive endeavor. Most of what physicians do involves the collection, review, and management of information. Examples of such activities include obtaining and recording patient information, consulting colleagues, reading the scientific literature, planning diagnostic procedures, devising strategies for patient care, interpreting tests, and conducting research. The ever-increasing biomedical knowledge base that must be considered to deliver optimal patient care only adds to the challenges facing medicine today. Successfully addressing these challenges to deliver the best health care possible requires not only the existence of valid and generalizable data sets derived from systematic basic, clinical, and epidemiological research efforts but also the ability to apply the knowledge derived from these research efforts at the point of care. It is easy to understand, therefore, why the field of biomedical informatics, a field that is concerned with collecting, managing, and optimally using information in health care and biomedicine, is critical to the current and future practice of medicine and the study of healthcare outcomes that result from such practice.1,2 Biomedical informatics approaches and related health information technology (health IT) platforms are key to enabling knowledge-driven healthcare and practice improvement initiatives based on a solid research foundation. Similar biomedical informatics approaches and resources are also critical to advancing outcomes research. Indeed, such technologies such as electronic health records (EHRs), clinical data repositories, and research-specific data management systems are already transforming the way we practice medicine and conduct research. This transformation is being further advanced by federally directed funding and research infrastructure development efforts.3,4 In the sections that follow, we provide an overview of how biomedical informatics and health IT processes and tools can affect the conduct of research and the delivery of evidence-based health care from our perspective. Given the current state of development …