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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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https://stemcells.nindsgenetics.org/

Cell sources currently include fibroblasts and/or induced pluripotent stem cells for Alzheimer's Disease, Amyotrophic Lateral Sclerosis (ALS), Ataxia-telangiectasia, Frontotemporal Lobar Degeneration (FTD), Huntington's Disease, Parkinson's Disease, and healthy controls. Cell sources, including isogenic cell lines for current and new diseases covered by the NINDS will be added over the next several years.

Proper citation: The NINDS Human Cell and Data Repository (NHCDR) (RRID:SCR_016319) Copy   


  • RRID:SCR_022197

    This resource has 10+ mentions.

https://vanvalen.github.io/about/

Software for segmenting individual cells in microscopy images using deep learning. Cell segmentation software.

Proper citation: DeepCell (RRID:SCR_022197) Copy   


  • RRID:SCR_024713

    This resource has 1+ mentions.

https://masst.gnps2.org/microbemasst/

Web taxonomically informed mass spectrometry search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging database of over 60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns.

Proper citation: microbeMASST (RRID:SCR_024713) 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_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_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_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_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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