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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.
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
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
Open-source toolkit that enables the rapid creation of tailored, web-enabled data storage and provides a cohesive system for data management, visualization, and processing. At its core, Midas Platform is implemented as a PHP modular framework with a backend database (PostGreSQL, MySQL and non-relational databases). While the Midas Platform system can be installed and deployed without any customization, the framework has been designed with customization in mind. As building one system to fit all is not optimal, the framework has been extended to support plugins and layouts. Through integration with a range of other open-source toolkits, applications, or internal proprietary workflows, Midas Platform offers a solid foundation to meet the needs of data-centric computing. Midas Platform provides a variety of data access methods, including web, file system and DICOM server interfaces, and facilitates extending the methods in which data is stored to other relational and non-relational databases.
Proper citation: Midas Platform (RRID:SCR_002186) Copy
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
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
http://www.geneatlas.org/gene/main.jsp
This website allows visitors to search for genes of interest based on their spatial expression patterns in the Postnatal Day 7 mouse brain. Geneatlas provides two searching tools: A graphical interface for customized spatial queries; A textual interface for querying annotated structures. Geneatlas is the product of a collaboration between researchers at Baylor College of Medicine, Rice University, and University of Houston.
Proper citation: Gene Atlas (RRID:SCR_008089) Copy
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
Transcription factor target database. Platform consolidating both computationally predicted and experimentally validated binding sites between transfer RNA-derived fragments and target genes or transcripts across multiple organisms.
Proper citation: tTFtarget (RRID:SCR_025631) Copy
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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