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Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.10.13.562258Source: DataRank Database

Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.

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Authors (5)

Alexandra A. Abu-Shmais ORCID,Michael Irvin,Matthew J. Vukovich ORCID,Ivelin S. Georgiev ORCID,Perry T. Wasdin ORCID

Live enrichment

PubMed:37904915 OpenAlex:https://openalex.org/W4387729808

National Institutes of Health

Grant: 5R01AI175245-02

Technologies for High-Throughput Mapping of Antigen Specificity to B-Cell-Receptor Sequence

National Institutes of Health

Grant: 5T15LM007450-18

Vanderbilt Biomedical Informatics Training Program

National Institutes of Health

Grant: 5T32AI112541-07

Chemical Biology of Infectious Diseases (CBID) Training Program

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/10/19/2023.10.13.562258.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.10.13.562258

repositoryhttps://doi.org/10.1101/2023.10.13.562258

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37904915

Fields of Study

T-cell and B-cell ImmunologySingle-cell and spatial transcriptomicsMonoclonal and Polyclonal Antibodies Research0206 medical engineering02 engineering and technology

Keywords

AntigenIdentification (biology)Noise (video)Negative binomial distributionComputer scienceComputational biologyCluster analysisAntibodyArtificial intelligenceBiologyPattern recognition (psychology)ImmunologyStatisticsMathematicsArticle