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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   

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http://cbio.mskcc.org/

Computational biology research at Memorial Sloan-Kettering Cancer Center (MSKCC) pursues computational biology research projects and the development of bioinformatics resources in the areas of: sequence-structure analysis; gene regulation; molecular pathways and networks, and diagnostic and prognostic indicators. The mission of cBio is to move the theoretical methods and genome-scale data resources of computational biology into everyday laboratory practice and use, and is reflected in the organization of cBio into research and service components ~ the intention being that new computational methods created through the process of scientific inquiry should be generalized and supported as open-source and shared community resources. Faculty from cBio participate in graduate training provided through the following graduate programs: * Gerstner Sloan-Kettering Graduate School of Biomedical Sciences * Graduate Training Program in Computational Biology and Medicine Integral to much of the research and service work performed by cBio is the creation and use of software tools and data resources. The tools that we have created and utilize provide evidence of our involvement in the following areas: * Cancer Genomics * Data Repositories * iPhone & iPod Touch * microRNAs * Pathways * Protein Function * Text Analysis * Transcription Profiling

Proper citation: Computational Biology Center (RRID:SCR_002877) Copy   

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http://www.iscos.org.uk

ISCoS promotes the highest standard of care in the practice of spinal cord injury for men, women and children throughout the world. Through its medical and multi disciplinary team of Professionals ISCoS endeavours to foster education, research and clinical excellence. ISCoS has a membership of over 1,000 Clinicians and Scientists from 87 countries. They regularly update their knowledge at the Annual Scientific Meeting held in a different country each year. Goals of ISCoS: :- Serve as an international impartial, non-political and non-profit making association whose purpose is to study all problems relating to traumatic and non-traumatic lesions of the spinal cord. This includes causes, prevention, basic and clinical research, medical and surgical management, clinical practice, education, rehabilitation and social reintegration. This society will function in close collaboration with other national and international bodies, thereby encouraging the most efficient use of available resources. :- Provide a scientific exchange among its members and others by collecting and disseminating information through publications, correspondence, exhibits, regional and international seminars, symposia, conferences and otherwise. :- Advise, encourage, promote and when requested, assist in efforts to co-ordinate or guide research, development and evaluation activities related to spinal cord lesions throughout the world. :- Advise, encourage, guide and support the efforts of those responsible for the care of patients involved and when requested, correlate these activities throughout the world. :- Advise, encourage, guide and support the efforts of those responsible for the education and training of medical professionals and professionals allied to medicine and when requested, correlate these activities throughout the world.

Proper citation: International Spinal Cord Society (RRID:SCR_002908) Copy   

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http://www.crdamc.amedd.army.mil/behav-health/strong-star.aspx

A multidisciplinary and multi-institutional research consortium to develop and evaluate the most effective early interventions possible for the detection, prevention, and treatment of combatrelated posttraumatic stress disorder (PTSD) in activeduty military personnel and recently discharged veterans. Complementary investigations are focused on the root causes of PTSD, including biological factors that influence PTSD susceptibility and recovery; the influence of comorbid physical and psychological ailments; and the interaction of cognitive-behavioral therapies and pharmacologic treatments. The full cohort of STRONG STAR trials include: Treatment Studies, Biological Studies, Epidemiological Studies, and Preclinical Studies. STRONG STAR is currently conducting three clinical treatment trials at Carl R. Darnall Army Medical Center (CRDAMC). The studies are examining the effectiveness of Cognitive Processing Therapy (CPT), Prolonged Exposure Therapy (PE) and Cognitive Behavioral Therapy for Insomnia (CBTi) with active duty service members. Treatments are offered in individual, group, and online formats, and last from two to eight weeks. Study participants must be active duty service members who will remain in the Ft Hood area for at least 34 months to complete initial assessments and treatment programs. Referrals to the treatment studies can be made through a behavioral health provider or through selfreferral., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Strong Star (RRID:SCR_003132) Copy   

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http://purl.bioontology.org/ontology/MMO

An ontology designed to represent the variety of methods used to make qualitative and quantitative clinical and phenotype measurements both in the clinic and with model organisms.

Proper citation: Measurement Method Ontology (RRID:SCR_003373) Copy   

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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://purl.bioontology.org/ontology/IDOMAL

An application ontology to cover all aspects of malaria (clinical, epidemiological, biological, etc) as well as the intervention attempts to control it, extending the infectious disease ontology (IDO).

Proper citation: Malaria Ontology (RRID:SCR_003369) Copy   

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http://edoctoring.ncl.ac.uk/Public_site/

Online educational tool that brings challenging clinical practice to your computer, providing medical education that is engaging, challenging and interactive. While there is no substitute for real-life direct contact with patients or colleagues, research has shown that interactive online education can be a highly effective and enjoyable method of learning many components of clinical medicine, including ethics, clinical management, epidemiology and communication skills. eDoctoring offers 25 simulated clinical cases, 15 interactive tutorials and a virtual library containing numerous articles, fast facts and video clips. Their learning material is arranged in the following content areas: * Ethical, Legal and Social Implications of Genetic Testing * Palliative and End-of-Life Care * Prostate Cancer Screening and Shared Decision-Making

Proper citation: eDoctoring (RRID:SCR_003336) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/CMO

An ontology designed to be used to standardize morphological and physiological measurement records generated from clinical and model organism research and health programs.

Proper citation: Clinical Measurement Ontology (RRID:SCR_003291) Copy   

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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   

•   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   

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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   

•   Source: SciCrunch Registry

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   

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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://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

http://www.cbioc.com/en/

Commercial organization that discovers, validates & analyzes genomic biomarkers with a focus on body fluid samples. Take advantage of their proven expertise in biomarker signature development and speed up your biomarker studies.

Proper citation: Comprehensive Biomarker Center (RRID:SCR_003901) Copy   

•   Source: SciCrunch Registry