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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.

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On page 6 showing 101 ~ 109 out of 109 results
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  • RRID:SCR_016215

    This resource has 1+ mentions.

https://github.com/YuanXue1993/SegAN

Image analysis software for medical image segmentation. The software is fueled by an end-to-end adversarial neural network that generates segmentation label maps.

Proper citation: SegAN (RRID:SCR_016215) Copy   


http://ailun.stanford.edu/

Re-annotated gene expression / proteomics data from GEO by relating all probe IDs to Entrez Gene IDs once every three months, enabling you to find data from GEO, and compare them from different platforms and species. Platform Annotations adds the latest annotations to any uploaded probe / gene ID list file. Platform Comparison compares any two platforms to find corresponding probes mapping to the same gene. Cross-species mapping maps platform annotations to other species. Gene Search finds deposited platforms and samples in GEO that contain a list of genes. GPL ID Search finds the GPL ID (GEO platform ID) for your array. You can also download the latest annotations files for all arrays and their comprehensive universal gene identifier table, which relates all types of gene / protein / clone identifiers to Entrez Gene IDs for all species. Note: The database was last updated on 4/30/2011. They have successfully mapped 54932732 individual probes from 385099 GEO samples measuring 3519 GEO platforms across 217 species.

Proper citation: Array Information Library Universal Navigator (RRID:SCR_006967) Copy   


  • RRID:SCR_006921

    This resource has 10+ mentions.

http://virtualhumanembryo.lsuhsc.edu/

A digital image database of serially sectioned human embryos from the Carnegie Collection originally developed as a collaboration between embryologist Dr. Raymond Gasser at Louisiana State University Health Science Center (LSUHSC) and the Human Developmental Anatomy Center (HDAC) in Washington D.C. The aim of the project is to increase understanding of human embryology and to encourage study of human embryonic development by providing students and researchers with reliable resources for human embryo morphology. The VHE project has several components: * DREM: The Digitally Reproduced Embryonic Morphology (DREM) project, with funding from NICHD, project has produced 27 image databases of labeled serial sections from representative human embryos at each of the 23 Carnegie stages. These databases, together with animations and reconstructions of the embryos are available on DVD and CD. * HEIRLOOM: The HEIRLOOM Collection (Human Embryo Imaging and Reconstruction, Library Of Online Media) was funded by the National Library of Medicine to provide greater access to the DREM databases. NLM provided funding to set up this website and to produce additional 3D-reconstructions and animations that are included on the DREM disks. Original website, http://virtualhumanembryo.lsuhsc.edu/HEIRLOOM/heirloom.htm * EHD: Starting in 2011, The Endowment for Human Development (EHD) will also host the VHE databases. They have made the project accessible to everyone and include a comprehensive cataloging of all the terms used to label the embryos. Their website enables users to browse through the complete VHE atlas of human embryology, http://www.ehd.org/virtual-human-embryo/

Proper citation: Virtual Human Embryo (RRID:SCR_006921) Copy   


  • RRID:SCR_015668

    This resource has 100+ mentions.

https://www.vumc.org/cpm/center-precision-medicine-blog/medi-ensemble-medication-indication-resource

Medication indication software for primary and secondary uses of electronic medical record (EMR) data. MEDI was created based on multiple commonly used medication resources (RxNorm, MedlinePlus, SIDER 2, and Wikipedia ) and by leveraging both ontology and natural language processing (NLP) techniques.

Proper citation: MEDI (RRID:SCR_015668) Copy   


  • RRID:SCR_015031

    This resource has 100+ mentions.

https://metamap.nlm.nih.gov/

Program to map biomedical text to the UMLS Metathesaurus and to discover Metathesaurus concepts referred to in text based on symbolic, natural-language processing and computational-linguistic techniques.

Proper citation: MetaMap (RRID:SCR_015031) Copy   


  • RRID:SCR_002654

    This resource has 500+ mentions.

http://ccb.jhu.edu/software/glimmerhmm/

A gene finder based on a Generalized Hidden Markov Model (GHMM). Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models . Currently, GlimmerHMM's GHMM structure includes introns of each phase, intergenic regions, and four types of exons (initial, internal, final, and single).

Proper citation: GlimmerHMM (RRID:SCR_002654) Copy   


  • RRID:SCR_016307

    This resource has 1+ mentions.

http://amp.pharm.mssm.edu/X2K/

Software tool to produce inferred networks of transcription factors, proteins, and kinases predicted to regulate the expression of the inputted gene list by combining transcription factor enrichment analysis, protein-protein interaction network expansion, with kinase enrichment analysis. It provides the results as tables and interactive vector graphic figures.

Proper citation: eXpression2Kinases (RRID:SCR_016307) Copy   


  • RRID:SCR_027030

    This resource has 1+ mentions.

https://github.com/slowkoni/rfmix

Software tool for local ancestry and admixture inference. Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference.

Proper citation: RFMix (RRID:SCR_027030) Copy   


  • RRID:SCR_027602

https://cdemapper.clinicalnlp.org/

Software Common Data Elements (CDEs) mapping tool to bridge the gap between local data elements and National Institutes of Health (NIH) CDEs. Elasticsearch and Large Language Model (LLM)-powered mapping tool designed for biomedical and clinical researchers to efficiently map study variables to the NIH Common Data Elements (CDEs). It integrates essential and advanced services into a user-centered mapping workflow, allowing users to choose different mapping strategies based on their project's needs.Used for enhancing National Institutes of Health common data element use with large language models.

Proper citation: CDEMapper (RRID:SCR_027602) Copy   



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