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https://prismclinical.com/

Provides Prism softwares and services that integrate advanced imaging into the clinical workflow for brain mapping from acquisition through processing, visualization, and export to PACS. Prism integrates functional MRI, diffusion MRI, perfusion, MR spectroscopy, and PET/CT into a unified workflow for diagnosis and treatment planning in brain disorders. Processing is also available as a cloud deployed service, Prism Serve. Research support includes import/export for imaging and derived quantitative data in both DICOM and research formats such as AFNI and Trackviz.

Proper citation: Prism Clinical Imaging (RRID:SCR_016977) Copy   

•   Source: SciCrunch Registry

https://t1dexchange.org/research/biobank/

Collection of biological samples linked to participant medical data from individuals living with type 1 diabetes. Unifies samples and data from eight different clinical studies related to type 1 diabetes.

Proper citation: T1D Exchange Biobank (RRID:SCR_017195) Copy   

•   Source: SciCrunch Registry

https://glomcon.org

Consortium to bring together clinicians, pathologists, researchers, and biotech innovators to create scalable network of stakeholders interested in helping patients with glomerular kidney disease. Makes collective expertise of its members available for discussion of individual cases, provides infrastructure for biomarker studies, enables genomic research, and facilitates clinical trials.

Proper citation: Glomerular Disease Study & Trial Consortium (RRID:SCR_017264) Copy   

•   Source: SciCrunch Registry

https://www.daqcord.org/

Software tool for practical self assessment and reporting method for clinical research studies, to capture key information about data acquisition and quality control measures. Linked to dataset so that potential research collaborators can determine if data meets their needs and expectations.

Proper citation: DAQCORD (RRID:SCR_017395) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/TCGAbiolinks/

Software R Bioconductor package for integrative analysis with TCGA data.TCGAbiolinks is able to access National Cancer Institute Genomic Data Commons thorough its GDC Application Programming Interface to search, download and prepare relevant data for analysis in R.

Proper citation: TCGAbiolinks (RRID:SCR_017683) Copy   

•   Source: SciCrunch Registry

http://www.tumorbank.unibe.ch/

Tumorbank Bern - TBB collects high quality clinical samples since 2003 for translational research selected by expert pathologists under controlled conditions of normal and diseased tissue from different origin. The Tumor Bank is approved by the Ethical Commission of Bern, we only collect samples with written informed patient consent. Origin of Tissue: Thoracic Surgery, Gynecology, Urology, Visceral Surgery, Orthopedic Surgery, Head and Neck Surgery, Neurosurgery Tumorbank Bern TBB holds 12,000 samples from 3600 Patients. Please contact us to check if we have samples for your field of research.

Proper citation: Tumorbank Bern (RRID:SCR_004611) Copy   

•   Source: SciCrunch Registry

http://www.nsabp.pitt.edu/NSABP_Pathology.asp

The NSABP (National Surgical Adjuvant Breast and Bowel Project) Tissue Bank is the central repository of tissue samples (stained and unstained slides, tissue blocks, and frozen tissue specimens) collected from clinical trials conducted by the NSABP. The main scientific aim of the NSABP Division of Pathology is to develop clinical context-specific prognostic markers and predictive markers that predict response to or benefit from specific therapeutic modality. To achieve this aim, the laboratory collects the tumor and adjacent normal tissues from cancer patients enrolled into the NSABP trials through its membership institutions, and maintain these valuable materials with clinical follow-up information and distribute them to qualified approved investigators. Currently, specimens from more than 90,000 cases of breast and colon cancer are stored and maintained at the bank. Paraffin embedded tumor specimens are available from NSABP trials. We currently do not bank frozen tissues. All blocks are from patients enrolled in prospective NSABP treatment protocols and complete clinical follow up information as well as demographic information is available. Depending on the project, unstained tissue sections of 4-micrometer thickness, tissue microarrays, or stained slides are provided to the investigators in a blinded study format. Any investigators with novel projects that conform to the research goals of NSABP may apply for the tissue. Please refer to the NSABP Tissue Bank Policy to determine if your project conforms to these goals. Priority is given to NSABP membership institutions who regularly submit tissue blocks.

Proper citation: National Surgical Adjuvant Breast and Bowel Project Tissue Bank (RRID:SCR_004506) Copy   

•   Source: SciCrunch Registry

http://sciblogs.co.nz/

Sciblogs brings together the best science bloggers in the country (New Zealand) on one website, creating a hub for scientific analysis and discussion and facilitating reader interaction. The website is for scientists who want to reach out to a general audience to explain their science and how it relates to society. Some Sciblog contributors spend most of their time in the lab or buried in research. Others are authors or entrepreneurs. All of them know what they are talking about and have an interest in engaging in discussion on the big science-related issues facing society. Over time more bloggers will be added to the Sciblogs roster. If you would like to inquire about hosting a blog on Sciblogs contact us. You can easily keep an eye on new Sciblogs posts by subscribing via RSS or email or by following our Twitter feed. Alternatively, there is a Facebook page as well as a Facebook group feel free to join in! Categories: * Science * Agriculture * Technology * Health and Medicine * Environment and Ecology * Science and Society

Proper citation: Sciblogs (RRID:SCR_005219) Copy   

•   Source: SciCrunch Registry

http://omniBiomarker.bme.gatech.edu

omniBiomarker is a web-application for analysis of high-throughput -omic data. Its primary function is to identify differentially expressed biomarkers that may be used for diagnostic or prognostic clinical prediction. Currently, omniBiomarker allows users to analyze their data with many different ranking methods simultaneously using a high-performance compute cluster. The next release of omniBiomarker will automatically select the most biologically relevant ranking method based on user input regarding prior knowledge. The omniBiomarker workflow * Data: Gene Expression * Algorithms: Knowledge-Driven Gene Ranking * Differentially expressed Genes * Clinical / Biological Validation * Knowledge: NCI Thesaurus of Cancer, Cancer Gene Index * back to Algorithms

Proper citation: omniBiomarker (RRID:SCR_005750) Copy   

•   Source: SciCrunch Registry

http://compbio.charite.de/phenomizer/

The Phenomizer offers three different approaches to find the appropriate term for a phenotypic abnormality, indicated by the three tabs on the left hand side: Feature, Disease and Ontology. The Phenomizer is intended to be used by qualified and licensed physicians in order to provide assistance in reaching the correct diagnosis in patients with hereditary diseases and for use as a teaching aid. The Phenomizer does not make diagnoses. Rather, it produces a ranked list of possibilities that can be used by physicians as a part of the diagnostic workup. The Phenomizer does not contain information about all possible diagnoses or even all possible hereditary diseases. The Phenomizer should not be used to make medical decisions without the advice of a physician.

Proper citation: Phenomizer (RRID:SCR_006157) Copy   

•   Source: SciCrunch Registry

https://www.radc.rush.edu/res/ext/home.htm

An Alzheimer's disease center which researches the cause, treatment and prevention of Alzheimer's disease with a focus on four main areas of research: risk factors for Alzheimer's and related disorders, the neurological basis of the disease, diagnosis, and treatment. Data includes a number of computed variables that are available for ROS, MAP and MARS cohorts. These variables are under categories such as affect and personality, chronic medical conditions, and clinical diagnosis. Specimens include ante-mortem and post-mortem samples obtained from subjects evaluated by ROS, MAP and clinical study cores. Specimen categories include: Brain tissue (Fixed and frozen), Spinal cord, Muscles (Post-mortem), and Nerve (Post-mortem), among other types of specimens. Data sharing policies and procedures apply to obtaining ante-mortem and post-mortem specimens from participants evaluated by the selected cohorts of the RADC.

Proper citation: Rush Alzheimer's Disease Center (RRID:SCR_008763) Copy   

•   Source: SciCrunch Registry

http://adc.med.nyu.edu/

The NYU Alzheimer's Disease Center is part of the Department of Psychiatry at New York University School of Medicine. The center's goals are to advance current knowledge and understanding of brain aging and Alzheimer's disease, to expand the numbers of scientists working in the field of aging and Alzheimer's research, to work toward better treatment options and care for patients, and to apply and share its findings with healthcare providers, researchers, and the general public. The ADC's programs and services extend to other research facilities and to healthcare professionals through the use of its core facilities. The NYU ADC is made up of seven core facilities: Administrative Core, Clinical Core, Neuropathology Core, Education Core, Data Management and Biostatistics Core, Neuroimaging Core, and Psychosocial Core.

Proper citation: NYU Alzheimer's Disease Center (RRID:SCR_008754) Copy   

•   Source: SciCrunch Registry

http://www.nia.nih.gov/research/dgcg/clinical-research-study-investigators-toolbox

The purpose of the NIA Clinical Research Toolbox is to provide a Web-based information repository for investigators and staff involved in clinical research. The Toolbox contains templates, sample forms, guidelines, regulations and information materials to assist investigators in the development and conduct of high quality clinical research studies.

Proper citation: Clinical Research Study Investigators Toolbox (RRID:SCR_008815) Copy   

•   Source: SciCrunch Registry

http://www.cori.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 5, 2022. Endoscopic Reporting Software, aggregated and individual research data and tailor-made services aimed to advance the overall practice of endoscopy. It was developed to study outcomes of gastrointestinal (GI) endoscopic procedures in real life settings, using data obtained from the CORI Endoscopic Reporting Software or from other endoscopic reporting software. Practice sites include hospitals, ambulatory care centers, private practices, universities, and Veteran''''s hospitals (VA''''s). The CORI v4 Endoscopic Reporting Software is a specialty Electronic Health Record used to document endoscopic procedures and provide reporting services to your practice. Data from participating providers is also sent to a central data repository to become part of the National Endoscopic Database (NED), which now contains data from over 2.7 million GI procedures. The CORI v4 Endoscopic Reporting Software offers significant benefits for participating practices, providers and patients, as well as for everyone who benefits from CORI''''s research efforts. You may actively participate in research with CORI. If you have ideas for research using the NED, their research team can help you evaluate those ideas, collect and analyze the data. In addition, you may choose to participate in one of the prospective research projects conducted by CORI research staff.

Proper citation: Clinical Outcomes Research Initiative (RRID:SCR_009010) Copy   

•   Source: SciCrunch Registry

https://portal.dbmi.hms.harvard.edu/projects/GRDR/

Data repository of de-identified patient data, aggregated in a standardized manner, to enable analyses across many rare diseases and to facilitate various research projects, clinical studies, and clinical trials. The aim is to facilitate drug and therapeutics development, and to improve the quality of life for the many millions of people who are suffering from rare diseases. The goal of GRDR is to enable analyses of data across many rare diseases and to facilitate clinical trials and other studies. During the two-year pilot program, a web-based template will be developed to allow any patient organization to establish a rare disease patient registry. At the conclusion of the program, guidance will be available to patient groups to establish a registry and to contribute de-identified patient data to the GRDR repository. A Request for Information (RFI) was released on February 10, 2012 requesting information from patient groups about their interest in participating in a GRDR pilot project. ORDR selected 30 patient organizations to participate in this pilot program to test the different functionalities of the GRDR. Fifteen (15) organizations with established registries and 15 organizations that do not have patient registry. The 15 patient groups, each without a registry, were selected to assist in testing the implementation of the ORDR Common Data Elements (CDEs) in the newly developed registry infrastructure. These organizations will participate in the development and promotion of a new patient registry for their rare disease. The GRDR program will fund the development and hosting of the registry during the pilot program. Thereafter, the patient registry is expected to be self-sustaining.The 15 established patient registries were selected to integrate their de-identified data into the GRDR to evaluate the data mapping and data import/export processes. The GRDR team will assist these organizations in mapping their existing registry data to the CDEs. Participating registries must have a means to export their de-identified registry data into a specified data format that will facilitate loading the data into the GRDR repository on a regular basis. The GRDR will also develop the capability to link patients'''' data and medical information to donated biospecimens by using a Voluntary Global Unique Patient Identifier (GUID). The identifier will enable the creation of an interface between the patient registries that are linked to biorepositories and the Rare Disease Human Biospecimens/Biorepositories (RD-HUB) http://biospecimens.ordr.info.nih.gov/.

Proper citation: GRDR (RRID:SCR_008978) Copy   

•   Source: SciCrunch Registry

http://www.biospace.com/

Online community for industry news and careers for life science professionals.

Proper citation: BioSpace (RRID:SCR_012010) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   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   

•   Source: SciCrunch Registry