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Private comprehensive research university in South Korea. Starting as a church-run kindergarten in 1918, CAU transformed into a school for female kindergarten teachers in 1922 and was granted university status in 1953.
Proper citation: Chung-Ang University; Seoul; South Korea (RRID:SCR_003547) Copy
A web based social authoring and publishing environment that adheres to open standards and RESTful design principals. It provides wiki-like ease of use with a sophisticated web services framework for rapid application development, creating flexible workflows and rapid integration. MindTouch creates a vibrant real-time information fabric by federating content from across enterprise silos, such as CRM, ERP, file servers, email, databases, web services and more.
Proper citation: Mindtouch DekiWiki (RRID:SCR_003425) Copy
http://www.nanostring.com/products/nSolver
Data analysis software program that offers nCounter users the ability to QC, normalize, and analyze data without having to purchase additional software packages.
Proper citation: nSolver Analysis Software (RRID:SCR_003420) Copy
http://portal.ncibi.org/gateway/mimiplugin.html
The Cytoscape MiMI Plugin is an open source interactive visualization tool that you can use for analyzing protein interactions and their biological effects. The Cytoscape MiMI Plugin couples Cytoscape, a widely used software tool for analyzing bimolecular networks, with the MiMI database, a database that uses an intelligent deep-merging approach to integrate data from multiple well-known protein interaction databases. The MiMI database has data on 119,880 molecules, 330,153 interactions, and 579 complexes. By querying the MiMI database through Cytoscape you can access the integrated molecular data assembled in MiMI and retrieve interactive graphics that display protein interactions and details on related attributes and biological concepts. You can interact with the visualization by expanding networks to the next nearest neighbors and zooming and panning to relationships of interest. You also can perceptually encode nodes and links to show additional attributes through color, size and the visual cues. You can edit networks, link out to other resources and tools, and access information associated with interactions that has been mined and summarized from the research literature information through a biology natural language processing database (BioNLP) and a multi-document summarization system, MEAD. Additionally, you can choose sub-networks of interest and use SAGA, a graph matching tool, to match these sub-networks to biological pathways.
Proper citation: MiMI Plugin for Cytoscape (RRID:SCR_003424) Copy
http://graphml.graphdrawing.org/
A file format for graphs that consists of a language core to describe the structural properties of a graph and a flexible extension mechanism to add application-specific data. It is based on XML and is ideally suited as a common denominator for all kinds of services generating, archiving, or processing graphs. Its main features include support of * directed, undirected, and mixed graphs, * hypergraphs, * hierarchical graphs, * graphical representations, * references to external data, * application-specific attribute data, and * light-weight parsers.
Proper citation: GraphML (RRID:SCR_003545) Copy
http://www.broadinstitute.org/mpg/magenta/
A computational tool that tests for enrichment of genetic associations in predefined biological processes or sets of functionally related genes, using genome-wide genetic data as input.
Proper citation: MAGENTA (RRID:SCR_003422) Copy
http://cran.r-project.org/web/packages/NAPPA/
Software that enables the processing and normalization of the standard mRNA data output from the Nanostring nCounter software.
Proper citation: NAPPA (RRID:SCR_003419) Copy
http://core.biotech.hawaii.edu/Bioinformatics.htm
THIS RESOURCE IS NO LONGER IN SERVCE, documented January 28, 2019. Core Facility provides the software and support for computer assisted protein and DNA sequence analysis and database access. The Genetics Computer Group GCG-Wisconsin package is currently available on PBRC's UNIX platform that is accessible via modem or direct connection. The package can be accessed via three interfaces: the command-line interface (UNIX C-shell), the web-based interface (SeqWeb) and the X-Windows based graphics interface (SeqLab). Applications in the package include sequence editing, alignment, comparison, primer design, restriction analysis, mapping, data presentation, database browsing, etc. In addition to local databases, access to remote databases (BLAST) is integrated into the package. The local databases are updated quarterly. Databases available include GenBank, EMBL, PIR-Protein, SWISS-PROT and Restriction Enzymes (REBASE).
Proper citation: GCG/SeqWeb (RRID:SCR_003454) Copy
http://rgd.mcw.edu/tools/ontology/ont_search.cgi
Ontology that defines hierarchical display of different rat strains as derived from parental strains. Ontology Browser allows to retrieve all genes, QTLs, strains and homologs annotated to particular term. Covers all types of biological pathways including altered and disease pathways, and to capture relationships between them within hierarchical structure. Five nodes of ontology include classic metabolic, regulatory, signaling, drug and disease pathways. Ontology allows for standardized annotation of rat. Serves as vehicle to connect between genes and ontology reports, between reports and interactive pathway diagrams, between pathways that directly connect to one another within diagram or between pathways that in some fashion are globally related in pathway suites and suite networks.
Proper citation: Rat Strain Ontology (RRID:SCR_003449) Copy
https://code.google.com/p/fade/
A software package designed to determine the methylation parameter at each cytosine or cytosine-guanine position in the human genome. FadE uses color reads produced by the SOLiD sequencer or nucleotide reads produced by the Illumina or 454 sequencing platforms.
Proper citation: FadE (RRID:SCR_003448) Copy
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
A biomedical ontology in the area of vaccine adverse events aimed to represent and analyze various vaccine-specific adverse events. OVAE is an extension of the Ontology of Adverse Events (OAE) and the Vaccine Ontology (VO).
Proper citation: Ontology of Vaccine Adverse Events (RRID:SCR_003442) Copy
https://code.google.com/p/proteomecommons-tranche/
A distributed file storage system that you can upload files to and download files from. All files uploaded to the repository are replicated several times to protect against their accidental loss. Files uploaded to the repository can be of any size, can be of any file type, and can be encrypted with a passphrase of your choosing. The Proteome Commons Tranche repository is the first instance of a Tranche repository. Tranche, was created so that anybody can take it and make their own Tranche repository. This is the first implementation of the Tranche software, and is useful as a test bed for the software. This repository relies on educational institutions to provide the hardware and facilities for Tranche servers. While we maintain a set of servers, the continued growth of this public resource will rely on the generosity of the institutions that use the repository most.
Proper citation: Proteome Commons Tranche repository (RRID:SCR_003441) Copy
The Cancer Text Information Extraction System (caTIES) provides tools for de-identification and automated coding of free-text structured pathology reports. It also has a client that can be used to search these coded reports. The client also supports Tissue Banking and Honest Broker operations. caTIES focuses on two important challenges of bioinformatics * Information extraction (IE) from free text * Access to tissue. Regarding the first challenge, information from free-text pathology documents represents a vital and often underutilized source of data for cancer researchers. Typically, extracting useful data from these documents is a slow and laborious manual process requiring significant domain expertise. Application of automated methods for IE provides a method for radically increasing the speed and scope with which this data can be accessed. Regarding the second challenge, there is a pressing need in the cancer research community to gain access to tissue specific to certain experimental criteria. Presently, there are vast quantities of frozen tissue and paraffin embedded tissue throughout the country, due to lack of annotation or lack of access to annotation these tissues are often unavailable to individual researchers. caTIES has three goals designed to solve these problems: * Extract coded information from free text Surgical Pathology Reports (SPRs), using controlled terminologies to populate caBIG-compliant data structures. * Provide researchers with the ability to query, browse and create orders for annotated tissue data and physical material across a network of federated sources. With caTIES the SPR acts as a locator to tissue resources. * Pioneer research for distributed text information extraction within the context of caBIG. caTIES focuses on IE from SPRs because they represent a high-dividend target for automated analysis. There are millions of SPRs in each major hospital system, and SPRs contain important information for researchers. SPRs act as tissue locators by indicating the presence of tissue blocks, frozen tissue and other resources, and by identifying the relationship of the tissue block to significant landmarks such as tumor margins. At present, nearly all important data within SPRs are embedded within loosely-structured free-text. For these reasons, SPRs were chosen to be coded through caTIES because facilitating access to information contained in SPRs will have a powerful impact on cancer research. Once SPR information has been run through the caTIES Pipeline, the data may be queried and inspected by the researcher. The goal of this search may be to extract and analyze data or to acquire slides of tissue for further study. caTIES provides two query interfaces, a simple query dashboard and an advanced diagram query builder. Both of these interfaces are capable of NCI Metathesaurus, concept-based searching as well as string searching. Additionally, the diagram interface is capable of advanced searching functionalities. An important aspect of the interface is the ability to manage queries and case sets. Users are able to vet query results and save them to case sets which can then be edited at a later time. These can be submitted as tissue orders or used to derive data extracts. Queries can also be saved, and modified at a later time. caTIES provides the following web services by default: MMTx Service, TIES Coder Service
Proper citation: caTIES - Cancer Text Information Extraction System (RRID:SCR_003444) Copy
http://www.compgen.org/tools/metagen
Software program providing a method for meta-analysis of case-control genetic association studies using random-effects logistic regression.
Proper citation: metagen (RRID:SCR_003443) Copy
http://code.google.com/p/omrse/
An ontology covering the domain of social entities that are related to health care, such as demographic information (social entities for recording gender (but not sex) and marital status, for example) and the roles of various individuals and organizations (patient, hospital, etc.)
Proper citation: Ontology of Medically Related Social Entities (RRID:SCR_003439) Copy
MIT's Department of Brain and Cognitive Sciences stands at the nexus of neuroscience, biology and psychology. We combine these disciplines to study specific aspects of the brain and mind including: vision, movement systems, learning and memory, neural and cognitive development, language and reasoning. Together, MIT's Department of Brain and Cognitive Sciences offers extraordinary learning opportunities for undergraduate, graduate and postdoctoral students. Because the human brain is immensely complex in many different ways at once, we pursue every level of inquiry - from molecules to cells to circuits to the mystery of the mind itself. As we study its diseases and disorders, its development and daily feats, like vision, speech, movement and memory, we also integrate methods and insights from every area of brain research. This unusual diversity of expertise fosters an intensely creative atmosphere that sparks startling collaborations. Already known for remarkable contributions to the field, our faculty members continually stretch the limits of knowledge, and bring the same passion to educating our exceptional students. A key part of the BCS mission is to offer its graduate and undergraduate students an educational experience of the highest quality. Our graduate students benefit from the impressive range of our program, and from the ability to participate in research projects with faculty members who are leaders in their fields. The department's undergraduate program, which includes both neuroscience and cognitive science, is one of the fastest-growing majors at MIT.
Proper citation: Massachusetts Institute of Technology; Department of Brain and Cognitive Sciences (RRID:SCR_003437) Copy
http://www.physiol.ucl.ac.uk/research/silver_a/nclamp/
Data acquisition software that runs in conjunction with neuromatic. configured to work with Igor Pro on PC or Mac, instrutech or national instrument acquisition devices. Funded by The Medical Research Council (UK) Compatibility with WaveMetrics Igor Pro 5 and 6. Compatibility with Mac or PC. NIDAQ interfacing multifunction DAQ boards from National Instruments. Requires Igor NIDAQ Tools MX. ITC interfacing - data acquisition systems from InstruTech / Heka. Requires Igor ITC XOPs. Episodic acquisition (stim and sample). Continuous acquisition (currently for ITC users only). Online analysis, with ability to create your own analysis functions. Notes and Log Files which can be displayed within a table or notebook. Automatic saving of data and log folders to your hard drive. Flexible stimulus pulse generator with ability to use your own pulse waveforms. Instant accessibility to all of NeuroMatics and Igor Pros pre-existing data analysis functions.
Proper citation: Nclamp - data acquisition software for electrophysiology (RRID:SCR_003750) Copy
Initiative to improve health by speeding up the development of, and patient access to, innovative medicines, particularly in areas where there is an unmet medical or social need. It does this by initiating and managing consortia composed of the key players involved in healthcare research, including universities, the pharmaceutical and other industries, small and medium-sized enterprises (SMEs), patient organizations, and medicines regulators. IMI is a public-private partnership between the European Union and the European pharmaceutical industry, represented by the European Federation of Pharmaceutical Industries and Associations (EFPIA), with a timeframe separated into two phases (2008-2013, 2014-2024) that are each defined by unique research agendas. The first phase (2008 2013) had four pillars that defined the focus of its research agenda: * Predicting safety: evaluating the safety of a compound during the pre-clinical phase of the development process and the later phases in clinical development. * Predicting efficacy: improving the ability to predict how a drug will interact in humans and how it may produce a change in function. * Knowledge management: utilization of information and data for predicting safety and efficacy. * Education and training: closing existing training gaps in the drug development process. Some of the consortia managed under IMI focused on specific health issues while others focused on broader challenges in drug development. Additionally, IMI launched a number of education and training projects during its first phases. The goal of the second phase (IMI2, 2014-2024) is to develop next generation vaccines and drugs. The focus is on delivering the right prevention and treatment for the right patient at the right time. There is a strong focus on the development of new medicines with an emphasis on tools and methods that accelerate patient access to new medicines. IMI2's agenda can be defined by four axes of research: * target validation and biomarker research (efficacy and safety) * adoption of innovative clinical trial paradigms * innovative medicines * patient-tailored adherence programs As part of its distinct goals, IMI2 aims to deliver: * 30% better success rate in clinical trials of priority medicines identified by the WHO; clinical proof of concept in immunological, respiratory, neurological and neurodegenerative diseases in five years; * new and approved diagnostic markers for four of these diseases and at least two new medicines which could either be new antibiotics or new therapies for Alzheimer's disease.
Proper citation: Innovative Medicines Initiative (RRID:SCR_003754) Copy
A consortium that focuses their efforts on discovering and developing new pharmaceutical drugs, vaccines (both preventive and therapeutic) and diagnostics against the infectious diseases that are prevalent in developing countries. The initial targets are those disorders designated by WHO as neglected tropical diseases prevalent in developing nations. The consortium aims to facilitate international partnerships that enable Japanese technology, innovations, and insights to play a more direct role in improving global health. Another goal of GHIT is to develop a new drug-discovery screening platform to assist the screening of compound libraries housed within Japanese companies and academic institutions. The vision is to have Japanese research organizations donate compounds, with the Fund reimbursing screening costs and leveraging screening programs of existing product-development partners.
Proper citation: Global Health Innovative Technology Fund (RRID:SCR_003753) Copy
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