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https://cabig.nci.nih.gov/tools/caTRIP

THIS RESOURCE IS NO LONGER IN SERVICE documented June 4, 2013. Allows users to query across a number of caBIG data services, join on common data elements (CDEs), and view results in a user-friendly interface. With an initial focus on enabling outcomes analysis, caTRIP allows clinicians to query across data from existing patients with similar characteristics to find treatments that were administered with success. In doing so, caTRIP can help inform treatment and improve patient care, as well as enable the searching of available tumor tissue, enable locating patients for clinical trials, and enable investigating the association between multiple predictors and their corresponding outcomes such as survival caTRIP relies on the vast array of open source caBIG applications, including: * Tumor Registry, a clinical system that is used to collect endpoint data * cancer Text Information Extraction System (caTIES), a locator of tissue resources that works via the extraction of clinical information from free text surgical pathology reports. while using controlled terminologies to populate caBIG-compliant data structures * caTissue CORE, a tissue bank repository tool for biospecimen inventory, tracking, and basic annotation * Cancer Annotation Engine (CAE), a system for storing and searching pathology annotations * caIntegrator, a tool for storing, querying, and analyzing translational data, including SNP data Requires Java installation and network connectivity.

Proper citation: caTRIP (RRID:SCR_003409) Copy   

•   Source: SciCrunch Registry

http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

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https://www.calindex.org/

A three-year consortium that brings together insurers and health care providers to share information from approximately 9 million patients, with a goal that insights from the data will bring down healthcare costs and improve outcomes. It aims to be one of the largest health information exchanges in the country, with the goal of better connecting the vast, often disparate healthcare landscape across California. The database that will house patient data will be overseen by Orion Health, an independent eHealth software company. The information will only be used for clinical purposes. Academic research institutions can apply to use the Cal INDEX de-identified data for research to benefit the public good, such as population health initiatives. Cal INDEX has five main goals: * Improve the quality of care by providing clinicians with a unified statewide source of integrated patient information * Provide patients with a seamless transition between health plans or across various healthcare professionals and hospitals * Improve efficiency and reduce the cost of healthcare * Encourage healthcare technology innovation * Improve public health by providing de-identified data for medical research. Cal INDEX plans to launch at the end of 2014 with approximately 9 million health information records from combined members of Dignity Health and Blue Shield of California and Anthem Blue Cross. Cal INDEX is open to any health data contributor. Cal INDEX will establish a bi-directional data interface with providers to exchange data with EMRs and other hospital and office-based systems.

Proper citation: California Integrated Data Exchange (RRID:SCR_003747) Copy   

•   Source: SciCrunch Registry

http://www.aditecproject.eu/

A consortium that aims to accelerate the development of immunization technologies for the next generation of human vaccines. The goals are to characterize the mode of action and conduct comparative effectiveness studies of: adjuvants, vectors, formulations, delivery devices, routes of immunization, homologous and heterologous primeboost schedules, on vaccine efficacy. As part of these clinical trials, the consortium will also investigate the impact of host factors such as age, gender, genetics and pathologies. The consortium hopes to use insights gained from their projects to advance the development of next-generation vaccines, using tools such as standardized animal models to select promising immunization technologies. The intended outcome of this partnership is to improve the vaccine development process by advancing: basic research, new technology development, and clinical trial methods. Scientific objectives: # Development of adjuvants, vectors, formulations, and delivery devices # Selection of candidates, routes of immunization, and prime-boost combinations in animal models # Assessment of the impact of host factors in response to vaccination # Development of concepts and tools from human immunization # Development of concepts and tools to address regulatory and ethical issues posed by novel immunization technologies # Creation of an internationally recognized training program for translational immunology and vaccinology. Data is shared across the research partners within and between the different workstreams. Additionally, the consortium has plans to create a clinical database that combines phenotypic and clinical information to study the immune response to influenza vaccination at a population level, in an effort to advance studies into the effects of genetic background, gender, and disease on vaccine response.

Proper citation: Advanced Immunization Technologies (RRID:SCR_003741) Copy   

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http://www.abirisk.eu/

A consortium that seeks to provide an integrated approach to anti-drug immunization by evaluating immunogenicity in hemophilia A, multiple sclerosis, and inflammatory diseases, and exploring new tools for protein drug immunogenicity. The data collected will be pooled in a single immunogenicity databank and will be standardized and used to develop models of anti-drug antibodies. By examining the correlation between patient and clinical factors and the incidence of immunogenicity, it hopes to reduce the regulatory and resource burdens of immunogenicity testing. The objectives of the consortium are: # Access to large cohorts of patients treated with marketed biopharmaceutical products # Complementary expertise for anti-drug antibodies (ADA) assays; standardization and characterization of ADA # Novel integrated approaches to characterize anti-drug lymphocyte responses # Development and validation of innovative prediction tools # Collection and integration of immunogenicity-related data and clinical relevance of ADA ABIRISK is grouped into five working projects, which communicate with one another and provide each other with results and data for analysis. The five working projects are: ADA assay development and validation and cohort management; cellular characterization and mechanisms of the AD immune response; evaluation and development of technologies for predicting immunogenicity; establishment of database, data analyses and integration; and project management and communication.

Proper citation: ABIRISK (RRID:SCR_003740) Copy   

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http://www.oncotrack.eu/

An international consortium to develop and assess novel approaches to identify and characterize biological markers for colon cancer that will deepen the understanding of the variable make-up of tumors and how this affects the way patients respond to treatment. They will use cutting edge laboratory-based genome sequencing techniques coupled to novel computer modelling approaches to study both the biological heterogeneity of colon cancers (i.e. patient to patient variability) as well as tumor variation within the patient for example, by comparing primary tumors with metastases. This five year project brings together top scientists from European academic institutions offering a wide range of expertise, and partners them with pharmaceutical companies. The project is based on the premise that this genetic and epigenetic information, combined with a description of the molecular pathology of the tumor, will allow OncoTrack to generate a more accurate in-silico model of the cancer cell. This will facilitate the identification of predictive markers that can be used to guide the optimal therapy strategy at the level of the individual patient - and will also provide on-going prognostic guidance for the clinician. This project will not only advance understanding of the fundamental biology of colon cancers but will provide the means and approach for the identification of previously undetected biomarkers not only in the cancer under study, but potentially also in other solid cancers and, in doing so, open the door for personalized management of the oncology patient.

Proper citation: OncoTrack (RRID:SCR_003767) Copy   

•   Source: SciCrunch Registry

http://c-path.org/programs/pkd/

Consortium to develop evidence supporting the use of imaging Total Kidney Volume (TKV) as a prognostic biomarker that predicts the progression of Autosomal Dominant Polycystic Kidney Disease (ADPKD) to select patients likely to respond to therapy into clinical trials. It aims to replace the currently used measurement of glomerular filtration rate (GFR). Scientists will use the data collected to develop a disease progression model that will evaluate the relationship between TKV and the known complications of ADPKD, including rate of loss of kidney function, hypertension, gross hematuria, kidney stones, urinary tract infections, development of end-stage renal disease, and mortality. These analyses will be used to support the regulatory qualification of TKV as an accepted measure for assessing the progression of ADPKD in clinical trials in which new therapies are tested. PKDOC has the following goals: # Develop standard clinical data elements and definitions that are specific to ADPKD # Create a database of aggregated data from existing multiple, longitudinal, and well-characterized research registries maintained over decades by the leading institutions in ADPKD clinical investigation # Advance and harmonize the missions of regulatory agencies by creating tools that help with the evaluation of new pharmaceutical compounds # Develop a quantitative disease progression model to examine the linkage between TKV and disease outcomes

Proper citation: Polycystic Kidney Disease Outcomes Consortium (RRID:SCR_003674) Copy   

•   Source: SciCrunch Registry

http://www.transceleratebiopharmainc.com/

Non-profit research organization aiming to accelerate drug development by increasing the quality and efficiency of clinical studies through the development of shared tools, methods, and platforms. Consortium partnerships are limited to pharmaceutical and biotechnology companies with research & development operations, although there are collaborations with external organizations such the Clinical Data Interchange Standards Consortium (CDISC). Its current focus is to collaborate on: * Standardizing risk-based monitoring * Development of methods to qualify and train clinical trial sites * Development of a common investigator web portal * Development of clinical data standards on efficacy, and methods for comparator drug trials It currently has 5 projects: # Standardized Approach for High-Quality, Risk-Based Monitoring program aims to develop an industry-wide standard and approach for risk-based monitoring of clinical trials in order to enhance patient safety and ensure the quality of clinical trial data. # Shared Site Qualification and Training program aims to standardize GCP training and site qualification credentials in order to realize efficiencies and accelerate study start-up timelines. # Common Investigator Site Portal is a platform designed to streamline investigator and site access through harmonized delivery of content and services. # Data Standards project is a partnership with CDISC to develop industry-wide data standards in priority therapeutic areas to support the exchange and submission of clinical research and meta-data, improving patient safety and outcomes. # Comparator Drugs project aims to establish reliable, rapid sourcing of quality products for use in clinical trials through a comparator supply model enabling accelerated trial timelines and enhanced patient safety.

Proper citation: TransCelerate BioPharma (RRID:SCR_003728) Copy   

•   Source: SciCrunch Registry

http://ncit.nci.nih.gov/

A reference terminology and core biomedical ontology for NCI that covers approximately 100,000 key biomedical concepts with terms, codes, definitions, and more than 200,000 inter-concept relationships. It is the reference terminology for NCI, NCI Metathesaurus and NCI informatics infrastructure covering vocabulary for clinical care, translational and basic research, and public information and administrative activities. It includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities; anatomy; agents, drugs and chemicals; genes and gene products and so on. In certain areas, like cancer diseases and combination chemotherapies, it provides the most granular and consistent terminology available. It combines terminology from numerous cancer research related domains, and provides a way to integrate or link these kinds of information together through semantic relationships. NCIt features: * Stable, unique codes for biomedical concepts; * Preferred terms, synonyms, definitions, research codes, external source codes, and other information; * Links to NCI Metathesaurus and other information sources; * Over 200,000 cross-links between concepts, providing formal logic-based definition of many concepts; * Extensive content integrated from NCI and other partners, much available as separate NCIt subsets * Updated frequently by a team of subject matter experts. NCIt is a widely recognized standard for biomedical coding and reference, used by a broad variety of public and private partners both nationally and internationally including the Clinical Data Interchange Standards Consortium Terminology (CDISC), the U.S. Food and Drug Administration (FDA), the Federal Medication Terminologies (FMT), and the National Council for Prescription Drug Programs (NCPDP).

Proper citation: NCI Thesaurus (RRID:SCR_003563) Copy   

•   Source: SciCrunch Registry

http://www.themmrf.org/research-programs/commpass-study/

A personalized medicine initiative to discover biomarkers that can better define the biological basis of multiple myeloma to help stratify patients. This effort hopes to obtain samples from approximately 1,000 multiple myeloma patients and follow them over time to identify how a patient's genetic profile is related to clinical progression and treatment response. As a partnership between 17 academic centers, 5 pharmaceuticals and the Department of Veterans Affairs, the goal of this eight year study is to create a database that can accelerate future clinical trials and personalized treatment strategies. MMRF's CoMMpass Study has the following goals: * Create a guide to which treatments work best for specific patient subgroups. * Share data with researchers to accelerate drug development for specific subtypes of multiple myeloma patients. In order to facilitate discoveries and development related to targeted therapies, the comprehensive data from CoMMpass is placed in an open-access research portal. The data will be part of the Multiple Myeloma Research Foundation's (MMRF) Personalized Medicine Platform combines CoMMpass data with those collected from MMRF's Genomics Initiative. It is hoped that the longitudinal data, combined with the annotated bio-specimens will help provide insights that can accelerate personalized therapies.

Proper citation: MMRF CoMMpass Study (RRID:SCR_003721) Copy   

•   Source: SciCrunch Registry

https://ftp.ncbi.nlm.nih.gov/pub/mhc/mhc/Final%20Archive/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 23, 2019 Database was open, publicly accessible platform for DNA and clinical data related to human Major Histocompatibility Complex (MHC). Data from IHWG workshops were provided as well., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: dbMHC (RRID:SCR_002302) Copy   

•   Source: SciCrunch Registry

http://www.clsi.org/

A not-for-profit membership organization that brings together the global laboratory community to foster excellence in laboratory medicine by facilitating the development of clinical laboratory testing standards based on input from and consensus among industry, government, and health care professionals. CLSI is setting the standard for quality in clinical laboratory testing around the world.

Proper citation: Clinical and Laboratory Standards Institute (RRID:SCR_002382) Copy   

•   Source: SciCrunch Registry

http://bioinf.uta.fi/base_root/

IDbases are locus-specific databases for immunodeficiency-causing mutations. Our aim is to establish database for every immunodeficiency or provide links to those maintained elsewhere. IDbases contain in addition to gene mutation, also information about clinical presentation. Information has been collected from literature as well as received directly from researchers. It would be most glad if those analyzing mutations would send their information by using the interactive web submission available in each database. A number of articles have been published related to IDbases. IDbases are curated and distributed with proprietary MUTbase software suite.

Proper citation: IDbases (RRID:SCR_002378) Copy   

•   Source: SciCrunch Registry

https://pdbp.ninds.nih.gov

Common data management resource and web portal to promote discovery of Parkinson's Disease diagnostic and progression biomarker candidates for early detection and measurement of disease progression. PDBP will serve as multi-faceted platform for integrating existing biomarker efforts, standardizing data collection and management across these efforts, accelerating discovery of new biomarkers, and fostering and expanding collaborative opportunities for all stakeholders.

Proper citation: Parkinson’s Disease Biomarkers Program Data Management Resource (PDBP DMR) (RRID:SCR_002517) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/gap

Database developed to archive and distribute clinical data and results from studies that have investigated interaction of genotype and phenotype in humans. Database to archive and distribute results of studies including genome-wide association studies, medical sequencing, molecular diagnostic assays, and association between genotype and non-clinical traits.

Proper citation: NCBI database of Genotypes and Phenotypes (dbGap) (RRID:SCR_002709) Copy   

•   Source: SciCrunch Registry

http://www.dana.org

A private philanthropy with principal interests in brain science, immunology, and education. The portal provides general information about the brain and current brain research, links to validated sites related brain disorders, education resources and lesson plans, and support for the training of in-school arts specialists. The Dana Foundation science and health grants support brain research in neuroscience and immunology and their interrelationship in human health and disease. The grant sections include brain and immuno-imaging, clinical neuroscience research, human immunology and neuroimmunology. The Foundation also occasionally sponsors workshops and forums for working scientists, as well as offering funding for selected young researchers to continue their education or to attend seminars and workshops elsewhere.

Proper citation: Dana Foundation (RRID:SCR_002789) Copy   

•   Source: SciCrunch Registry

http://www.pharmgkb.org/

Database and central repository for genetic, genomic, molecular and cellular phenotype data and clinical information about people who have participated in pharmacogenomics research studies. The data includes, but is not limited to, clinical and basic pharmacokinetic and pharmacogenomic research in the cardiovascular, pulmonary, cancer, pathways, metabolic and transporter domains. PharmGKB welcomes submissions of primary data from all research into genes and genetic variation and their effects on drug and disease phenotypes. PharmGKB collects, encodes, and disseminates knowledge about the impact of human genetic variations on drug response. They curate primary genotype and phenotype data, annotate gene variants and gene-drug-disease relationships via literature review, and summarize important PGx genes and drug pathways. PharmGKB is part of the NIH Pharmacogenomics Research Network (PGRN), a nationwide collaborative research consortium. Its aim is to aid researchers in understanding how genetic variation among individuals contributes to differences in reactions to drugs. A selected subset of data from PharmGKB is accessible via a SOAP interface. Downloaded data is available for individual research purposes only. Drugs with pharmacogenomic information in the context of FDA-approved drug labels are cataloged and drugs with mounting pharmacogenomic evidence are listed.

Proper citation: PharmGKB (RRID:SCR_002689) Copy   

•   Source: SciCrunch Registry

http://www.mrn.org/

Non-profit organization focused on imaging technology that is dedicated to advancing the diagnosis and treatment of mental illness and brain injury. MRN consists of an interdisciplinary association of scientists located at universities, national laboratories and research centers around the world and is focused on imaging technology and its emergence as an integral element of neuroscience investigation. The MRNs initial plan called for the building of state-of-the-art magnetic resonance imaging (MRI) and magnetoencephalogram (MEG) neuroimaging systems to be applied to studies of mental illness. This important task was carried out by Minds initial collaborators: Massachusetts General Hospitals Martinos Biomedical Imaging Center (Harvard and MIT), the University of Minnesota, the University of New Mexico, and Los Alamos National Laboratory. Since both the Network and the mission have expanded beyond building neuroimaging tools, a comprehensive understanding of mental illness and more fundamental and systematic understanding of the brain, is possible. The MRN Mobile Imaging system is a custom designed one-of-a-kind facility.

Proper citation: Mind Research Network (RRID:SCR_002925) Copy   

•   Source: SciCrunch Registry

http://www.celgene.com

An American global biotechnology company that manufactures drug therapies for cancer and inflammatory disorders. The company's major products are Thalomid (thalidomide), which is approved for the acute treatment of the cutaneous manifestations of moderate to severe erythema nodosum leprosum (ENL), as well as in combination with dexamethasone for patients with newly diagnosed multiple myeloma, and Revlimid (lenalidomide), for which the company has received FDA and EMA approval in combination with dexamethasone for the treatment of multiple myeloma patients who have received at least one prior therapy. Revlimid is also approved in the United States for the treatment of patients with transfusion-dependent anemia due to Low- or Intermediate-1-risk Myelodysplastic syndromes (MDS) associated with a deletion 5q cytogenetic abnormality with or without additional cytogenetic abnormalities. Both Thalomid and Revlimid are sold through proprietary risk-management distribution programs to ensure safe and appropriate use of these pharmaceuticals. Vidaza is approved for the treatment of patients with MDS. Celgene also receives royalties from Novartis Pharma AG on sales of the entire Ritalin family of drugs, which are widely used to treat Attention Deficit Hyperactivity Disorder (ADHD). (Adapted from Wikipedia) There are numerous clinical trials at major medical centers using compounds from Celgene. Investigational compounds are being studied for patients with incurable hematological and solid tumor cancers, including multiple myeloma, myelodysplastic syndromes, chronic lymphocyte leukemia (CLL), non-Hodgkin's lymphoma (NHL), glioblastoma, and ovarian, pancreatic and prostate cancer.

Proper citation: Celgene (RRID:SCR_002955) Copy   

•   Source: SciCrunch Registry

http://nbc.jhu.edu/

This center provides routine behavioral/cognitive testing of mice with phenotypes that are expressed as a consequence of alterations at the level of gene function, and that are relevant to basic neuroscience and to animal models of neurological and psychiatric disorders. Current Research Behavioral testing within the center involves a collaborative component in which mice provided by users are assessed for behavioral/cognitive functions. All research includes behavioral assessment of a variety of genetically altered mice provided by users. Services Provided The objective of the center is to provide a link between genetic and molecular analysis of neural function and the study of integrative systems and clinical conditions through behavioral assessment of animal models, and mouse behavioral phenotypes generated by genetic modification. Sponsors: This resource is supported by the National Center of Research Resources (Grant Number: P40 RR017688).

Proper citation: Neurogenetics and Behavior Center (RRID:SCR_002851) Copy   

•   Source: SciCrunch Registry