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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

http://www.stanford.edu/group/exonarray/cgi-bin/plot_selector.pl

Transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes. Provides improved cell-type-specific markers for better understanding of neural development, function, and disease.

Proper citation: Exon Array Browser (RRID:SCR_008712) 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://genome-cancer.ucsc.edu/

A suite of web-based tools to visualize, integrate and analyze cancer genomics and its associated clinical data. It is possible to display your own clinical data within one of their datasets.

Proper citation: UCSC Cancer Genomics Browser (RRID:SCR_011796) Copy   

•   Source: SciCrunch Registry

http://cbbiweb.uthscsa.edu/KMethylomes/

Datbase and web-based system for visualization and analysis of genome-wide methylation data of human cancers.

Proper citation: Cancer Methylome System (RRID:SCR_012013) Copy   

•   Source: SciCrunch Registry

http://www.cravat.us/

A web-based application designed with an easy-to-use interface to facilitate the high-throughput assessment and prioritization of genes and missense alterations important for cancer tumorigenesis.

Proper citation: CRAVAT (RRID:SCR_012776) Copy   

•   Source: SciCrunch Registry

http://cancer.osu.edu/research/cancerresearch/sharedresources/ltb/Pages/index.aspx

The OSU Comprehensive Cancer Center Leukemia Tissue Bank Shared Resource (LTBSR) facilitates the successful translation of basic leukemia research to the clinical setting via an extensive repository of tissue samples and accompanying pathologic, cytogenetic and clinical data for ready correlation of clinical and biological results. The LTBSR, which is an NCI-sponsored biorepository, has more than 40,000 vials of cryopreserved viable cells and 13,000 vials of matched frozen plasma and/or serum samples from more than 4,000 patients treated for leukemia and other malignancies. Committed to furthering translational research efforts for OSUCCC - James members and the cancer research community, the LTBSR provides investigators with training and technical support as well as procurement, processing, storage, retrieval and distribution of clinical research materials. In many cases, the LTBSR serves as the central processing lab for multi-site trials in which the principal investigator is an OSUCCC - James member. The LTBSR's goals are to: * Provide a central collection, processing and a state-of-the-art repository for samples collected from leukemia patients treated on OSUCCC - James protocols, and * Provide materials to investigators involved in collaborative studies with OSU, who examine relevant cellular and molecular properties of leukemia and correlate these properties with clinical or population-based outcomes.

Proper citation: Ohio State Leukemia Tissue Bank (RRID:SCR_000529) Copy   

•   Source: SciCrunch Registry

http://www.cpctr.net/

THIS RESOURCE IS NO LONGER IN SERVICE. Doumented on September 23,2022. The National Cancer Institute initially established the Cooperative Prostate Cancer Tissue Resource (CPCTR) to provide prostate cancer tissue samples with clinical annotation to researchers. The Resource provides access to formalin-fixed, paraffin-embedded primary prostate cancer tissue with associated clinical and follow-up data for research studies, particularly studies focused on translating basic research findings into clinical application. Fresh-frozen tissue is also available with limited clinical follow up information since these are more recent cases. The Resource database contains pathologic and clinical information linked to a large collection of prostate tissue specimens that is available for research. Researchers can determine whether the Resource has the tissues and patient data they need for their individual research studies. Consultation and interpretive services: Assistance is available from trained CPCTR pathologists. The CPCTR can provide consultative assistance in staining interpretation, and scoring, on a collaborative basis. Fresh Frozen and Paraffin Tissue: The resource has over 7,000 annotated cases (including 7,635 specimens and 38,399 annotated blocks). Tissue Microarrays (TMA): The CPCTR has slides from prostate cancer TMAs with associated clinical data. The information provided for each case on the arrays (derived from radical prostatectomy specimens) includes: age at diagnosis, race, PSA at diagnosis, tumor size, TNM stage, Gleason score and grade, and vital status and other variables.

Proper citation: CPCTR: Cooperative Prostate Cancer Tissue Resource (RRID:SCR_000803) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

•   Source: SciCrunch Registry

https://www.cancermodels.org/

Cancer research platform that aggregates clinical, genomic and functional data from various types of patient derived cancer models, xenographs, organoids and cell lines. Open catalog of harmonised patient-derived cancer models. Standardises, harmonises and integrates clinical metadata, molecular and treatment-based data from academic and commercial providers worldwide. Data is FAIR and underpins generation and testing of new hypotheses in cancer mechanisms and personalised medicine development. PDCM Finder have expanded to organoids and cell lines and is now called CancerModels.Org. PDCM Finder was launched in April 2022 as successor of PDX Finder portal, which focused solely on patient-derived xenograft models.

Proper citation: CancerModels.Org (RRID:SCR_023931) Copy   

•   Source: SciCrunch Registry

http://rnainformatics.org.cn/RiboToolkit/

Integrated web server developed for Ribo-seq data analysis. Platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution.Web based service to centralize Ribo-seq data analyses, including data cleaning and quality evaluation, expression analysis based on RPFs, codon occupancy, translation efficiency analysis, differential translation analysis, functional annotation, translation metagene analysis, and identification of actively translated ORFs.

Proper citation: RiboToolkit (RRID:SCR_024406) Copy   

•   Source: SciCrunch Registry

https://seer.cancer.gov/csr/1975_2016/

Platform to report outlining trends in cancer statistics and methods to derive various cancer statistics from the Surveillance, Epidemiology, and End Results (SEER) program. Authoritative source for cancer statistics in the United States.

Proper citation: NCI SEER Cancer Statistics Review (RRID:SCR_024685) Copy   

•   Source: SciCrunch Registry

https://seer.cancer.gov/lymphomarecode/lymphoma-2020.html

Website describing International Classification of Diseases codes that corresponds to lymphomas in the Surveillance, Epidemiology, and End Results (SEER) registry.

Proper citation: NCI Lymphoid Neoplasm Recode 2020 Revision Definition (RRID:SCR_024686) Copy   

•   Source: SciCrunch Registry

https://openmm.org/

Software toolkit to run modern molecular simulations. It can be used either as a standalone application for running simulations, or as a library that enables accelerated calculations for molecular dynamics on high-performance computer architectures.

Proper citation: OpenMM (RRID:SCR_000436) Copy   

•   Source: SciCrunch Registry

http://www.karmanos.org/

Center for patient care, education and research on cancer. The institute focuses its research on prevention methods, early detection, treatment and finding cures.

Proper citation: Karmanos Cancer Institute (RRID:SCR_000508) Copy   

•   Source: SciCrunch Registry

http://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

•   Source: SciCrunch Registry

http://dctd.cancer.gov/

Division of NCI that takes prospective cancer detection and treatment leads, facilitates their paths to clinical application, and expedites the initial and subsequent large-scale testing of new agents, biomarkers, imaging tests, and other therapeutic interventions (radiation, surgery, immunotherapy) in patients. DCTD, like all of NCI, supports many programs that could not be done without government funding - investigators supported by the division engage in scientifically sound, high-risk research that may yield great benefits for patients with cancer, but are too difficult or risky for industry or academia to pursue. This includes a particular emphasis on the development of distinct molecular signatures for cancer, refined molecular assays, and state-of-the-art imaging techniques that will guide oncologic therapy in the future. The division has eight major programs that work together to bring unique molecules, diagnostic tests, and therapeutic interventions from the laboratory bench to the patient bedside: * Cancer Diagnosis Program * Cancer Imaging Program * Cancer Therapy Evaluation Program * Developmental Therapeutics Program * Radiation Research Program * Translational Research Program * Biometrics Research Branch * Office of Cancer Complementary and Alternative Medicine

Proper citation: DCTD (RRID:SCR_004196) Copy   

•   Source: SciCrunch Registry

http://www.bionet.umn.edu/tpf/home.html

Procure and distribute human tissue and other biological samples in support of basic, translational, and clinical cancer research at the University of Minnesota. The TPF is a centralized resource with standardized patient consent, sample collection, processing, storage, quality control, distribution, and electronic record maintenance. Since the 1996 inception of the TPF, over 61,000 tissue samples including well-preserved samples of malignant and benign tumors, organ-matched normal tissue, and other types of diseased tissues, have been collected from surgical specimens obtained at the University of Minnesota Medical Center-Fairview (UMMC-F) University Campus. Surgical pathologists are intellectually engaged in TPF functions, providing researchers with specimen-oriented medical consultation to facilitate research productivity. Prior to surgery, TPF personnel identify and consent patients for procurement of tissue, blood, urine, saliva, and ascites fluid. Within the integrated working environment of the surgical pathology laboratory, freshly obtained tissues not needed for diagnosis are selected and provided by pathologists to TPF personnel. Tissue samples are then assigned an independent code and processed. TPF staff can also work with researchers to individualize the procurement of tissues to fit specific research needs.

Proper citation: University of Minnesota Tissue Procurement Facility (RRID:SCR_004270) Copy   

•   Source: SciCrunch Registry

http://acsr.ucsf.edu/

A biorepository for HIV-infected human biospecimens from a wide spectrum of HIV-related or associated diseases, including cancer, and from appropriate HIV-negative controls. The ACSR has formalin-fixed paraffin embedded biospecimens, fresh frozen biospecimens, malignant cell suspensions, fine needle aspirates, and cell lines from patients with HIV-related malignancies. It also contains serum, plasma, urine, bone marrow, cervical and anal specimens, saliva, semen, and multi-site autopsy speicmens from patients with HIV-related malignancies including those who have participated in clinical trials. The ACSR has an associated databank that contains prognostic, staging, outcome and treatment data on patients from whom tissues were obtained. The ACSR database contains more than 300,000 individual biospecimens with associated clinical information. Biospecimens are entered into the ACSR database by processing type, disease category, and number of cases defined by disease category.

Proper citation: AIDS and Cancer Specimen Resource (RRID:SCR_004216) Copy   

•   Source: SciCrunch Registry

http://pluto3.nci.nih.gov/tissue/default.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. The Specimen Resource Locator is a database to help researchers locate human specimens (tissue, serum, DNA/RNA, other specimens) for cancer research. It includes tissue banks and tissue procurement systems with access to normal, benign, precancerous and cancerous human tissue from a variety of organs. Researchers specify the types of specimens, number of cases, preservation methods and associated data they require. The Locator will then search the database and return a list of tissue resources most likely to meet their requirements. When no match is obtained, the researcher is referred to the NCI Tissue Expediter ([email protected]). The Tissue expediter is a scientist who can help researchers identify appropriate resources and/or appropriate collaborators.

Proper citation: NCI Specimen Resource Locator (RRID:SCR_004754) Copy   

•   Source: SciCrunch Registry