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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.
http://scratch.proteomics.ics.uci.edu/
Web tool as sequence-based, alignment-free and pathogen-independent predictor of protein antigenicity.Predicts likelihood that protein is protective antigen. Integrated in SCRATCH suite of predictors.
Proper citation: ANTIGENpro (RRID:SCR_018779) Copy
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
https://github.com/epistasislab/hibachi
Software tool that creates data sets with particular characteristics. Method and open source software for simulating complex biological and biomedical data to aid in comparing and evaluating machine learning methods.
Proper citation: Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (RRID:SCR_017140) Copy
Software tool as robust preprocessing pipeline for functional MRI.Used for preprocessing of diverse fMRI data.
Proper citation: fMRIPrep (RRID:SCR_016216) Copy
https://github.com/lanagarmire/lilikoi
Software tool as an R package for personalized pathway-based classification modeling using metabolomics data. Provides personalized pathway deregulation measurements (PDS scores) and offers a standardized classification model for biomarker prediction.
Proper citation: lilikoi (RRID:SCR_016361) Copy
https://open.med.harvard.edu/display/SHRINE/Community
Software providing a scalable query and aggregation mechanism that enables federated queries across many independently operated patient databases. This platform enables clinical researchers to solve the problem of identifying sufficient numbers of patients to include in their studies by querying across distributed hospital electronic medical record systems. Through the use of a federated network protocol, SHRINE allows investigators to see limited data about patients meeting their study criteria without compromising patient privacy. This software should greatly enable population-based research, assessment of potential clinical trials cohorts, and hypothesis formation for followup study by combining the EHR assets across the hospital system. In order to obtain the maximum number of cases representing the study population, it is useful to aggregate patient facts across as many sites as possible. Cutting across institutional boundaries necessitates that each hospital IRB remain in control, and that their local authority is recognized for each and every request for patient data. The independence, ownership, and legal responsibilities of hospitals predetermines a decentralized technical approach, such as a federated query over locally controlled databases. The application comes with the SHRINE Core Ontology but it can be used with any ontology, even one that is disease specific. The Core Ontology is designed to enable the widest range of studies possible using facts gathered in the EMR during routine patient care. SHRINE allows multiple ontologies to be used for different research purposes on the same installed systems.
Proper citation: SHRINE (RRID:SCR_006293) Copy
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
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://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
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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