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https://github.com/QTIM-Lab/DeepNeuro

Software Python package for neuroimaging data. Framework to design and train neural network architectures. Used in medical imaging community to ensure consistent performance of networks across variable users, institutions, and scanners.

Proper citation: DeepNeuro (RRID:SCR_016911) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/omicsGAN

Software generative adversarial network to integrate two omics data and their interaction network to generate one synthetic data corresponding to each omics profile that can result in better phenotype prediction. Used to capture information from interaction network as well as two omics datasets and fuse them to generate synthetic data with better predictive signals.

Proper citation: OmicsGAN (RRID:SCR_022976) Copy   

•   Source: SciCrunch Registry

http://www.zfishbook.org/NGP/journalcontent/SCORE/SCORE.html

Narrative resource describing a visual data analysis and collection approach that takes advantage of the cylindrical nature of the zebrafish allowing for an efficient and effective method for image capture called, Specimen in a Corrected Optical Rotational Enclosure (SCORE) Imaging. To achieve a non-distorted image, zebrafish were placed in a fluorinated ethylene propylene (FEP) tube with a surrounding, optically corrected imaging solution: water. By similarly matching the refractive index of the housing (FEP tubing) to that of the inner liquid and outer liquid (water), distortion was markedly reduced, producing a crisp imagable specimen that is able to be fully rotated 360 degrees. A similar procedure was established for fixed zebrafish embryos using convenient, readily available borosilicate capillaries surrounded by 75% glycerol. The method described could be applied to chemical genetic screening and other, related high-throughput methods within the fish community and among other scientific fields.

Proper citation: Zebrafish - SCORE Imaging: Specimen in a Corrected Optical Rotational Enclosure (RRID:SCR_001300) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/saga.html

SAGA (Substructure Index-based Approximate Graph Alignment) is a tool for querying a biological graph database to retrieve matches between subgraphs of molecular interactions and biological networks. SAGA implements an efficient approximate subgraph matching algorithm that can be used for a variety of biological graph matching problems such as the pathway matching SAGA uses to compare pathways in KEGG and Reactome. You can also use SAGA to find matches in literature databases that have been parsed into semantic graphs. In this use of SAGA, portions of PubMed have been parsed into graphs that have nodes representing gene names. A link is drawn between two genes if they are discussed in the same sentence (indicating there is potential association between the two genes). SAGA lets you match graphs between different databases even though the content is distinct and the databases organize pathways in different ways. This cross-database matching is achieved by SAGA's flexible approximate subgraph matching model that computes graph similarity, and allows for node gaps, node mismatches, and graph structural differences. Comparing pathways from different databases can be a useful precursor to pathway data integration. SAGA is very efficient for querying relatively small graphs, but becomes prohibitory expensive for querying large graphs. Large graph data sets are common in many emerging database applications, and most notably in large-scale scientific applications. To fully exploit the wealth of information encoded in graphs, effective and efficient graph matching tools are critical. Due to the noisy and incomplete nature of real graph datasets, approximate, rather than exact, graph matching is required. Furthermore, many modern applications need to query large graphs, each of which has hundreds to thousands of nodes and edges. TALE is an approximate subgraph matching tool for matching graph queries with a large number of nodes and edges. TALE employs a novel indexing technique that achieves a high pruning power and scales linearly with the database size.

Proper citation: Substructure Index-based Approximate Graph Alignment (RRID:SCR_003434) Copy   

•   Source: SciCrunch Registry

http://www.rhesusbase.org/drugDisc/CAM.jsp

OKCAM (Ontology-based Knowledgebase for Cell Adhesion Molecules) is an online resource for human genes known or predicted to be related to the processes of cell adhesion. These genes include members of the cadherin, immunoglobulin/FibronectinIII (IgFn), integrin, neurexin, neuroligin, and catenin families. Totally 496 human CAM genes were compiled and annotated. We have mapped these genes onto a novel cell adhesion molecule ontology (CAMO) that provides a hierarchical description of cell adhesion molecules and their functions. It is intended to provide a means to facilitate better and better understanding of the global and specific properties of CAMs through their genomic features, regulatory modes, expression patterns and disease associations become clearer. You may browse by CAM ontology, Chromosomes and Full Gene list.

Proper citation: OKCAM: Ontology-based Knowledgebase for Cell Adhesion Molecules (RRID:SCR_010696) Copy   

•   Source: SciCrunch Registry

https://painseq.shinyapps.io/harmonized_painseq_v1/

Harmonized cell atlases using sc/snRNA-seq data obtained from dorsal root ganglia and trigeminal ganglio mammalian datasets.

Proper citation: Harmonized DRG and TG Reference Atlas (RRID:SCR_025720) Copy   

•   Source: SciCrunch Registry

https://gseapy.readthedocs.io/en/latest/

Software Python package for performing gene set enrichment analysis. Used for characterizing gene expression changes by analysis of large single-cell datasets.

Proper citation: GSEApy (RRID:SCR_025803) Copy   

•   Source: SciCrunch Registry

https://github.com/smorabit/hdWGCNA

Software R package for performing weighted gene co-expression network analysis in high dimensional transcriptomics data such as single-cell RNA-seq or spatial transcriptomics.

Proper citation: hdWGCNA (RRID:SCR_027496) Copy   

•   Source: SciCrunch Registry

http://www.nihpromis.org/

Repository of person centered measures that evaluates and monitors physical, mental, and social health in adults and children.

Proper citation: Patient-Reported Outcomes Measurement Information System (RRID:SCR_004718) Copy   

•   Source: SciCrunch Registry

http://courses.jax.org/2012/addiction.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This course emphasizes genetic applications and approaches to drug addiction research through methodological instruction based on literature, data sets and informatics resources drawn from studies of addiction related phenotypes. The course includes plenary sessions on major progress in addiction genetics, and discussion sessions in which students present their work for discussion on applications of genetic methods. Students will leave the course able to design and interpret genetic and genomic studies of addiction as they relate to their specific research question, and will be able to make use of current bioinformatics resources to identify research resources and make use of public data sources in their own research.

Proper citation: Short Course on the Genetics of Addiction (RRID:SCR_005560) Copy   

•   Source: SciCrunch Registry

https://neurobiobank.nih.gov/

National resource for investigators utilizing human post-mortem brain tissue and related biospecimens for their research to understand conditions of the nervous system. Federated network of brain and tissue repositories in the United States that collects, evaluates, stores, and makes available to researchers, brain and other tissues in a way that is consistent with the highest ethical and research standards. The NeuroBioBank ensures protection of the privacy and wishes of donors. Provides information to the public about the need for tissue donation and how to register as a donor.

Proper citation: NIH NeuroBioBank (RRID:SCR_003131) Copy   

•   Source: SciCrunch Registry

http://www.drugabuseresearchtraining.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on November 07, 2012. Decemeber 15, 2011 - Thank you for your interest in DrugAbuseResearchTraining.org. The site, courses, and resources are no longer available. Please send an email to inquiry (at) md-inc.com if you would like to be notified if the site or courses become available again. Introduction to Clinical Drug and Substance Abuse Research Methods is an online training program intended to introduce clinicians and substance abuse professionals to basic clinical research methods. The program is divided into four modules. Each module covers an entire topic and includes self-assessment questions, references, and online resources: * The Neurobiology of Drug Addiction * Biostatistics for Drug and Substance Abuse Research * Evaluating Drug and Substance Abuse Programs * Designing and Managing Drug and Substance Abuse Clinical Trials The learning objectives of this program are to help you: * Evaluate the benefits of alternative investigative approaches for answering important questions in drug abuse evaluation and treatment. * Define the proper levels of measurement and appropriate statistical methods for a clinical study. * Address common problems in data collection and analysis. * Anticipate key human subjects and ethical issues that arise in drug abuse studies. * Interpret findings from the drug abuse research literature and prepare a clinical research proposal. * Prepare research findings for internal distribution or publication in the peer reviewed literature. * Recognize drug addiction as a cyclical, chronic disease. * Understand and describe the brain circuits that are affected by addicting drugs, and explain to others the effects of major classes of addicting drugs on brain neurotransmitters. * Utilize new pharmacologic treatments to manage persons with drug addiction. Physicians can earn AMA PRA Category 1 Credit and purchase a high resolution printable electronic CME certificate(view sample); non-physicians can purchase high resolution printable electronic certificate of course participation that references AMA PRA Category 1 credit (view sample). This program does not offer printed certificates.

Proper citation: Online Education for the International Research Community: AboutIntroduction to Clinical Drug and Substance Abuse Research Methods (RRID:SCR_000802) Copy   

•   Source: SciCrunch Registry

http://neurodata.io/

Project portal dedicated to understand animal and machine intelligence and repository of data and tools. Suite of tools to analyze and graph imaging data. Image and data repository for large, publicly available neuro-specific data files and images. Contains tools for analytics, databases, cloud computing, and Web-services applied to both big neuroimages and big neurographs.

Proper citation: neurodata (RRID:SCR_014264) Copy   

•   Source: SciCrunch Registry

http://www.wakeforestinnovations.com/technology-for-license/demon-voltammetry-and-analysis-software/

A software for performing fast scan cyclic voltammetry recordings in brain tissue for detection of neurotransmitters. It was written in the LabView programming language and can be used to provide command voltage to equipment and record the resulting waveforms. The analysis portion of the software can view and export data, apply noise filters, perform chemometric and waveform kinetic analysis, and create figures.

Proper citation: Demon Voltammetry and Analysis Software (RRID:SCR_014468) Copy   

•   Source: SciCrunch Registry

http://knowledge-space.org/

A community encyclopaedia that links brain research concepts with data, models and literature from around the world. It is an open project where users can participate and contribute to the global research community.

Proper citation: KnowledgeSpace (RRID:SCR_014539) Copy   

•   Source: SciCrunch Registry

http://cancercontrol.cancer.gov/tcrb/tturc/

A transdisciplinary approach to the full spectrum of basic and applied research on tobacco use to reduce the disease burden of tobacco use, including: * Etiology of tobacco use and addiction * Impact of advertising and marketing * Prevention of tobacco use * Treatment of tobacco use and addiction * Identification of biomarkers of tobacco exposure * Identification of genes related to addiction and susceptibility to harm from tobacco Goals * Increase the number of investigators from relevant disciplines who focus on the study of tobacco use as part of transdisciplinary teams. * Generate basic research evidence to improve understanding of the etiology and natural history of tobacco use. * Produce evidence-based tobacco use interventions that can translate to the community and specific understudied or underserved populations. * Increase the number of evidence-based interventions that are novel, including the development, testing and dissemination of innovative behavioral treatments and prevention strategies based upon findings from basic research. * Train transdisciplinary investigators capable of conducting cutting-edge tobacco use research. * Increase the number of peer-reviewed publications in the areas of tobacco use, nicotine addiction, and treatment.

Proper citation: Transdisciplinary Tobacco Use Research Centers (RRID:SCR_006858) Copy   

•   Source: SciCrunch Registry

http://bellsouthpwp.net/c/a/capowski//NTSPublic.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. A hardware and software package with which a scientist could trace the structure of neurons and other neuroscientific features directly from tissue sections or from a stack of their images into a computer. Then it also could edit, merge, filter, display in 3D, and make realistic plots of the structures. The NTS also includes a substantial statistical package that provided many, now standardized, mathematical and statistical summaries that described each neuron and compared one population to another. Additionally, NTS also provided an embryonic electrotonic modeler that simulates and displayes the electrical functioning of a cell. The NTS uses a special purpose graphics display processor called the VDP3 whose output is presented on a very high resolution CRT. During tracing, the VDP3 presents a variable-diameter cursor and other information directly in the microscope and enables tracing at a high spatial resolution and with measurement of process diameters limited only by the microscope''s optics. Control of tracing is done with a 3D joystick that allows easy control of five input variables: X,Y,Z position, cursor diameter, and a numeric tag. Finally, superb 3D interactive displays of completed cells are provided on the VDP3.

Proper citation: Eutectic NTS (RRID:SCR_008062) Copy   

•   Source: SciCrunch Registry

https://confluence.crbs.ucsd.edu/display/NIF/DRG

Gene expression data from published journal articles that test hypotheses relevant to neuroscience of addiction and addictive behavior. Data types include effects of particular drug, strain, or knock out on particular gene, in particular anatomical region. Focuses on gene expression data and exposes data from investigations using DNA microarrays, polymerase chain reaction, immunohistochemistry and in-situ hybridizations. Data are available for query through NIF interface.Data submissions are welcome.

Proper citation: Drug Related Gene Database (RRID:SCR_003330) Copy   

•   Source: SciCrunch Registry

http://sparkinsight.org

A clustering and visualization tool that enables the interactive exploration of genome-wide data, with a specialization in epigenomics data. Spark is also available as a service within the Epigenome toolset of the Genboree Workbench. The approach utilizes data clusters as a high-level visual guide and supports interactive inspection of individual regions within each cluster. The cluster view links to gene ontology analysis tools and the detailed region view connects to existing genome browser displays taking advantage of their wealth of annotation and functionality.

Proper citation: Spark (RRID:SCR_006207) Copy   

•   Source: SciCrunch Registry

https://www.phenxtoolkit.org/

Set of measures intended for use in large-scale genomic studies. Facilitate replication and validation across studies. Includes links to standards and resources in effort to facilitate data harmonization to legacy data. Measurement protocols that address wide range of research domains. Information about each protocol to ensure consistent data collection.Collections of protocols that add depth to Toolkit in specific areas.Tools to help investigators implement measurement protocols.

Proper citation: Phenotypes and eXposures Toolkit (RRID:SCR_006532) Copy   

•   Source: SciCrunch Registry