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http://www.vetmed.vt.edu/research/amrv.asp

An institutional training program to train veterinarians in conducting research. The program trains veterinarians in acquiring the skills of a researcher as they undergo a specific M.S. or Ph.D program. The program urges graduates to take part in research concerning animal models of infectious diseases, immunology, and nutrition, among other health topics.

Proper citation: Post-DVM Training Program on Animal Model Research for Veterinarians (RRID:SCR_008303) Copy   

•   Source: SciCrunch Registry

http://www.strout.net/conical/

CONICAL is a C++ class library for building simulations common in computational neuroscience. Currently its focus is on compartmental modeling, with capabilities similar to GENESIS and NEURON. Future classes may support reaction-diffusion kinetics and more. A key feature of CONICAL is its cross-platform compatibility; it has been fully co-developed and tested under Unix, DOS, and Mac OS. Any C++ compiler which adheres to the emerging ANSI standard should be able to compile the CONICAL classes without modification. It is intended to encourage the rapid development of simulator software, especially on non-Unix systems where such software is sorely lacking. The present focus of the CONICAL library of C++ classes is compartmental modeling. A model neuron is built out of compartments, usually with a cylindrical shape. When small enough, these open-ended cylinders can approximate nearly any geometry, just as the stack of cylinders approximates a cone in the logo above. While any compartment has passive electrical properties (like a simple resistor-capacitor circuit), more interesting properties require the use of active ion channels whose conductance varies as a function of the time or membrane voltage. A standard Hodgkin-Huxley ion channel is included as one of the built-in CONICAL object types. Most of the voltage-gated ion channels in the literature can be directly implemented merely by setting the parameters of this class. For extensibility, this class is derived from several layers of more general classes. Connections between neurons can be implemented in several ways. For a gap junction (i.e., simple electrical connection), a passive current (or pair of currents, one in each direction) can be used. Synapses are more complex objects, but used in a similar fashion. The Alpha-function synapse is a very popular model of synaptic transmission, and is a basic CONICAL class. More complex (and realistic) synapses can be built using the Markov-model synapse. (A Markov model can be used on its own for other purposes as well.) In addition to classes directly related to neural modeling, CONICAL contains several other useful object types. These include a current injector, and a column-oriented output stream for storing data in table form.

Proper citation: Conical: The Computational Neuroscience Class Library (RRID:SCR_008318) Copy   

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http://www.ini.uzh.ch/

The mission of the Institute is to discover the key principles by which brains work and to implement these in artificial systems that interact intelligently with the real world. The Institute of Neuroinformatics is built of many people covering a wide range of disciplines and research areas. The major research projects and areas are listed below. - Behavior and Cognition: At the Institute of Neuroinformatics researchers investigate in Behavior and Cognition on various levels, ranging from neuronal circuit models of learning and adaptation over psychophysical experiments for color constancy up to modeling complex behavioral tasks such as exploration and goal-directed navigation. - Computation in Neural Circuits: By examining the brains of cats, rats and monkeys, and by making simulations of the cortex, INI hopes to learn how this circuit performs such widely different tasks. This knowledge might lead to advances in how computers are designed, and will certainly lead to advances in the subtlety and power of medical neuroscience. - Neurotechnologies: INI aims to harness the principles of biological computation, which can be expected to have a major impact on the technology market as autonomous intelligence pervades equipment, vehicles, buildings, utilities and clothing. Sponsors: INI is supported by European Union (EU), Gerbert Ruf Stiftung, Neuroscience Center (ZNZ), Swiss Confederation (KTI), Swiss Federal Institute of Technology Zurich (ETH), Swiss National Science Foundation (SNF), University of Zurich (UZH), and VW Stiftung

Proper citation: Institute of Neuroinformatics (RRID:SCR_008331) Copy   

•   Source: SciCrunch Registry

http://www.opentox.org/dev/apis/api-1.1/structure

Tools for the integration of data from various sources (public and confidential), for the generation and validation of computer models for toxic effects, libraries for the development and seamless integration of new algorithms, and scientifically sound validation routines. The goal of OpenTox is to develop an interoperable predictive toxicology framework which may be used as an enabling platform for the creation of predictive toxicology applications. OpenTox is relevent for users from a variety of research areas: Toxicological and chemical experts (e.g. risk assessors, drug designers, researchers) computer model developers and algorithm developers non specialists requiring access to Predictive Toxicology models and data OpenTox applications can combine multiple web services providing users access to distributed toxicological resources including data, computer models, validation and reporting. Applications are based on use cases that satisfy user needs in predictive toxicology. OpenTox was initiated as a collaborative project involving a combination of different enterprise, university and government research groups to design and build the initial OpenTox framework. Additionally numerous organizations with industry, regulatory or expert interests are active in providing guidance and direction. The goal is to expand OpenTox as a community project enabling additional expert and user participants to be involved in developments in as timely a manner as possible. To this end, our mission is to carry out developments in an open and transparent manner from the early days of the project, and to open up discussions and development to the global community at large, who may either participate in developments or provide user perspectives. Cooperation on data standards, data integration, ontologies, integration of algorithm predictions from different methods, and testing and validation all have significant collaboration opportunities and benefits for the community. OpenTox is working to meet the requirements of the REACH legislation using alternative testing methods to contribute to the reduction of animal experiments for toxicity testing. Relevant international authorities (e.g., ECB, ECVAM, US EPA, US FDA) and industry organizations participate actively in the advisory board of the OpenTox project and provide input for the continuing development of requirement definitions and standards for data, knowledge and model exchange. OpenTox actively supports the development and validation of in silico models and algorithms by improving the interoperability between individual systems (common standards for data and model exchange), increasing the reproducibility of in silico models (by providing a quality source of structures, toxicity data and algorithms) and by providing scientifically sound and easy-to-use validation routines. OpenTox is committed to the support and integration of alternative testing methods using in vitro assay approaches, systems biology, stem cell technology, and the mining and analysis of human epidemiological data. Hence the framework design must take into account extensibility to satisfy a broad range of scientific developments and use cases.

Proper citation: OpenTox Framework (RRID:SCR_008686) Copy   

•   Source: SciCrunch Registry

http://psynova.com/

Commercial organization focused on the development and exploitation of novel biomarkers for psychiatric illnesses. They provide industrial and academic partners with comprehensive biomarker discovery services in commercial and collaborative projects. They operate in the field of psychiatric disorders and their products and services are designed to excel biomarker research. In 2010, Psynova Neurotech and its partner company Rules-Based-Medicine Inc (now MyriadRBM) conducted a beta launch of a blood test aiding in the diagnosis of schizophrenia (http://www.veripsych.com/). They are now refining the test and have shifted their focus to the development of new blood-based biomarker tests that aid in the diagnosis, prognosis and differential diagnosis of schizophrenia, bipolar disorder and major depression. They offer not only their pre-selceted Multiple Reaction Monitoring (MRM) and Multiplex Immunoassay products, but also custom build panels. If they are provided with a list of analyses to evaluate, they can produce an analytical panel according to individual needs utilizing either MRM or Multiplex Immunoassay technologies.

Proper citation: Psynova Neurotech (RRID:SCR_003959) Copy   

•   Source: SciCrunch Registry

http://www.ncdc.noaa.gov/paleo/softlib/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 12,2023. A simple, efficient, process-based forward model of tree-ring growth, requires as inputs only latitude and monthly temperature and precipitation.

Proper citation: VS-Lite (RRID:SCR_002431) Copy   

•   Source: SciCrunch Registry

http://www.ncdc.noaa.gov/paleo/softlib/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 12,2023. FORTRAN code for a simple hydrologic-isotopic-balance model for application to paleolake d18O records. Inputs to the model include discharge, on-lake precipitation, evaporation, and the d18O values of these fluid fluxes. Benson and Paillet (2002)

Proper citation: HIBAL (RRID:SCR_002461) Copy   

•   Source: SciCrunch Registry

http://www.ara.com/products/mppd.htm

Computational model that can be used for estimating human and rat airway particle dosimetry. The model is applicable to risk assessment, research, and education. The MPPD model calculates the deposition and clearance of monodisperse and polydisperse aerosols in the respiratory tracts of rats and human adults and children (deposition only) for particles ranging in size from ultrafine (0.01 micrometers) to coarse (20 micrometers). The models are based on single-path and multiple-path methods for tracking air flow and calculating aerosol deposition in the lung. The single-path method calculates deposition in a typical path per airway generation, while the multiple-path method calculates particle deposition in all airways of the lung and provides lobar-specific and airway-specific information. Within each airway, deposition is calculated using theoretically derived efficiencies for deposition by diffusion, sedimentation, and impaction within the airway or airway bifurcation. Filtration of aerosols by the nose and mouth is determined using empirical efficiency functions. The MPPD model includes calculations of particle clearance in the lung following deposition.

Proper citation: Multiple-Path Particle Dosimetry Model (RRID:SCR_001486) Copy   

•   Source: SciCrunch Registry

http://www.clarklabs.org/

Geospatial software for monitoring and modeling the Earth system. Includes tools for GIS, image processing, surface analysis, vertical applications for land change analysis and earth trends exploration, and more.

Proper citation: IDRISI (RRID:SCR_001696) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/BiGGR.html

Software package that provides an interface to simulate metabolic reconstruction from the BiGG database and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs.

Proper citation: BiGGR (RRID:SCR_002854) Copy   

•   Source: SciCrunch Registry

http://neuroml-db.org

The NeuroML database is a curated relational database that provides for the storage and retrieval of computational neuroscience models expressed in NeuroML, which is an extensible XML-based language for describing complex mathematical models of neurons and neuronal networks. NeuroML models are unique in their modular and multi-scale structure, where subcomponents of models can correspond to neuroscience models. In particular, the NeuroML database allows for efficient searches over the components of models and metadata that are associated with a hierarchical NeuroML model description.

Proper citation: NeuroML Database (RRID:SCR_013705) Copy   

•   Source: SciCrunch Registry

http://channelpedia.epfl.ch/

An information management framework for comprehensive ion channel information. It is a knowledge base system centered on genetically expressed ion channel models and it encourages researchers of the field to contribute, build and refine the information through an interactive wiki-like interface. It is web-based, freely accessible and currently contains 187 annotated ion channels with 50 Hodgkin-Huxley models (September 2014). Channelepdia provides an ideal platform to collectively build ion channel knowledge base by accommodating both structured and unstructured data. The current version of Channelpedia contains the following sections : Introduction, Genes, Ontologies, Interactions, Structure, Expression, Distribution, Function, Kinetics and Models. Newly published literature related to ion channels is automatically queried every week from PubMed and added to respective categories. Currently, Channelpedia contains ~180,000 abstracts related to ion channels from Pubmed.

Proper citation: ChannelPedia (RRID:SCR_003807) Copy   

•   Source: SciCrunch Registry

http://bigg.ucsd.edu/

A knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.

Proper citation: BiGG Database (RRID:SCR_005809) Copy   

•   Source: SciCrunch Registry

http://ecoli.naist.jp/GB8/

A database of high-throughput data being collected to understand comprehensively the living E. coli K-12 model cell. GenoBase is a public repository for sequence information, proteome, transcription, and metabolome data. The GenoBase contains columns labeled Gene, Synonym, ECK, Genome, ID, Left, Right, Direction, Description, Comment, and Status. The table displays two rows for each gene: one row shows data for the E. coli K-12 MG1655 genome; the other shows data for the E. coli K-12 W3110 genome. Left, Right, and direction give the coordinates and orientation of the gene. Search/Clip allows the user to find information in GenoBase based on gene, position, or DNA sequence. References is currently not fully operational. Other search allows execution of an SQL query.

Proper citation: GenoBase (RRID:SCR_007682) Copy   

•   Source: SciCrunch Registry

http://supfam.org/SUPERFAMILY/

SUPERFAMILY is a database of structural and functional protein annotations for all completely sequenced organisms. The SUPERFAMILY annotation is based on a collection of hidden Markov models, which represent structural protein domains at the SCOP superfamily level. A superfamily groups together domains which have an evolutionary relationship. The annotation is produced by scanning protein sequences from over 1,700 completely sequenced genomes against the hidden Markov models.

Proper citation: SUPERFAMILY (RRID:SCR_007952) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/asd/altsplice/index.html

AltSplice is a computer generated high quality data set of human transcript-confirmed splice patterns, alternative splice events, and the associated annotations. This data is being integrated with other data that is generated by other members of the ASD consortium. The ASD project will provide the following in its three year duration: -human curated database of alternative spliced genes and their properties -a computer generated database of alternatively spliced genes and their properties -the integration of the above and newly found knowledge in a user-friendly interface and research workbench for both bioinformaticists and biologists -DNA chips that are based on the data in the above databases -the DNA chips will be used to test against predisposition for and diagnoses of human diseases ASD aims to analyse this mechanism on a genome-wide scale by creating a database that contains all alternatively spliced exons from human, and other model species. Disease causing mutations seem to induce aberrations in the process of splicing and its regulation. The ASD consortium will develop a DNA microarray (chip) that contains cDNAs of all the splicing regulatory proteins and their isoforms, as well as a chip that contains a number of disease relevant genes. We will concentrate on three models of disease (breast cancer, FTDP-17, male infertility) in which a connection between mis-splicing and a pathological state has been observed. Finally, these chips will be developed as demonstrative kits to detect predisposition for and diagnosis of such diseases. Categories: Nucleotide Sequences: Gene Structure, Introns and Exons, & Splice Sites Databases

Proper citation: AltSplice Database of Alternative Spliced Events (RRID:SCR_008162) Copy   

•   Source: SciCrunch Registry

https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 29,2025. Electroencephalogram (EEG) data recorded from invasive and scalp electrodes. The EEG database contains invasive EEG recordings of 21 patients suffering from medically intractable focal epilepsy. The data were recorded during an invasive pre-surgical epilepsy monitoring at the Epilepsy Center of the University Hospital of Freiburg, Germany. In eleven patients, the epileptic focus was located in neocortical brain structures, in eight patients in the hippocampus, and in two patients in both. In order to obtain a high signal-to-noise ratio, fewer artifacts, and to record directly from focal areas, intracranial grid-, strip-, and depth-electrodes were utilized. The EEG data were acquired using a Neurofile NT digital video EEG system with 128 channels, 256 Hz sampling rate, and a 16 bit analogue-to-digital converter. Notch or band pass filters have not been applied. For each of the patients, there are datasets called ictal and interictal, the former containing files with epileptic seizures and at least 50 min pre-ictal data. the latter containing approximately 24 hours of EEG-recordings without seizure activity. At least 24 h of continuous interictal recordings are available for 13 patients. For the remaining patients interictal invasive EEG data consisting of less than 24 h were joined together, to end up with at least 24 h per patient. An interdisciplinary project between: * Epilepsy Center, University Hospital Freiburg * Bernstein Center for Computational Neuroscience (BCCN), Freiburg * Freiburg Center for Data Analysis and Modeling (FDM).

Proper citation: Electroencephalogram Database: Prediction of Epileptic Seizures (RRID:SCR_008032) Copy   

•   Source: SciCrunch Registry

http://chromium.lovd.nl/LOVD2/home.php?select_db=CDKN2A

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The CDKN2A Database presents the germline and somatic variants of the CDKN2A tumor suppressor gene recorded in human disease through June 2003, annotated with evolutionary, structural, and functional information, in a format that allows the user to either download it or manipulate it for their purposes online. The goal is to provide a database that can be used as a resource by researchers and geneticists and that aids in the interpretation of CDKN2A missense variants. Most online mutation databases present flat files that cannot be manipulated, are often incomplete, and have varying degrees of annotation that may or may not help to interpret the data. They hope to use CDKN2A as a prototype for integrating computational and laboratory data to help interpret variants in other cancer-related genes and other single nucleotide polymorphisms (SNPs) found throughout the genome. Another goal of the lab is to interpret the functional and disease significance of missense variants in cancer susceptibility genes. Eventually, these results will be relevant to the interpretation of single nucleotide polymorphisms (SNPs) in general. The CDKN2A locus is a valuable model for assessing relationships among variation, structure, function, and disease because: Variants of this gene are associated with hereditary cancer: Familial Melanoma (and related syndromes); somatic alterations play a role in carcinogenesis; allelic variants occur whose functional consequences are unknown; reliable functional assays exist; and crystal structure is known. All variants in the database are recorded according to the nomenclature guidelines as outlined by the Human Genome Variation Society. This database is currently designed for research purposes only and is not yet recommended as a clinical resource. Many of the mutations reported here have not been tested for disease association and may represent normal, non-disease causing polymorphisms.

Proper citation: CDKN2A Database (RRID:SCR_008179) Copy   

•   Source: SciCrunch Registry

http://www.lamhdi.org/

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Proper citation: LAMHDI: The Initiative to Link Animal Models to Human DIsease (RRID:SCR_008643) Copy   

•   Source: SciCrunch Registry

http://www.biology.emory.edu/research/Prinz/database/database.html

This page describes the contents of a database of 1.7 million model neurons. This database is available for interested researchers after contacting the creators, but is not web accessible. The construction and analysis of the database are described in detail in Prinz AA, Billimoria CP, Marder E (2003). Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90: 3998-4015. Because of its size (over 6 GB even in the zipped version), it is not practicable to download the database over the internet. Instead, we have made multiple copies of the database on sets of two DVDs each. We are happy to send a set of DVDs to anybody who is interested upon e-mail request to Astrid Prinz.

Proper citation: Crustacean stomatogastric model neuron database (RRID:SCR_008260) Copy   

•   Source: SciCrunch Registry