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James M Tiedje

Biographic Data

ID9080243
NAMEJames M Tiedje
GIVEN NAMESJames M
FAMILY NAMETiedje
SIGNATURETIEDJE J M
AFFILIATIONSMichigan State University
ORCID0000-0002-8992-6218
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2007
LATEST PUBLICATION YEAR2021
H-INDEX0
  • Artificial intelligence

    Open Access•Yongjun Xu, Xin Liu et al.•ARTICLE•The Innovation•2021

    Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promot…

  • Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

    Open Access•Qiong Wang, Wang Qiong et al.•ARTICLE•Applied and Environmental…•2007

    The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high…

No prominent works on this page.

  • Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

    Open Access•Qiong Wang, Wang Qiong et al.•ARTICLE•Applied and Environmental…•2007

    The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high…

  • Artificial intelligence

    Open Access•Yongjun Xu, Xin Liu et al.•ARTICLE•The Innovation•2021

    Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promot…

Artificial Intelligence (2 works) · Computer Science (2 works) · 16S ribosomal RNA (1 work) · Biology (1 work) · Classifier (UML) (1 work) · Cognitive science (1 work) · Computational biology (1 work) · COVID-19 diagnosis using AI (1 work) · Data science (1 work) · Ecology (1 work)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae