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Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly
Specified Certainty Classification (SCC) classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities. By allowing the classifier output to be less precise than one of a set of atomic decisions, SCC allows all decisions to achieve a specified level of cert...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452099/ https://www.ncbi.nlm.nih.gov/pubmed/34545333 |
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author | Karr, Alan F. Hauzel, Jason Menon, Prahlad Porter, Adam A. Schaefer, Marcel |
author_facet | Karr, Alan F. Hauzel, Jason Menon, Prahlad Porter, Adam A. Schaefer, Marcel |
author_sort | Karr, Alan F. |
collection | PubMed |
description | Specified Certainty Classification (SCC) classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities. By allowing the classifier output to be less precise than one of a set of atomic decisions, SCC allows all decisions to achieve a specified level of certainty, as well as provides insights into classifier behavior by examining all decisions that are possible. Our primary illustration is read classification for reference-guided genome assembly, but we demonstrate the breadth of SCC by also analyzing COVID-19 vaccination data. |
format | Online Article Text |
id | pubmed-8452099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cornell University |
record_format | MEDLINE/PubMed |
spelling | pubmed-84520992021-09-21 Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly Karr, Alan F. Hauzel, Jason Menon, Prahlad Porter, Adam A. Schaefer, Marcel ArXiv Article Specified Certainty Classification (SCC) classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities. By allowing the classifier output to be less precise than one of a set of atomic decisions, SCC allows all decisions to achieve a specified level of certainty, as well as provides insights into classifier behavior by examining all decisions that are possible. Our primary illustration is read classification for reference-guided genome assembly, but we demonstrate the breadth of SCC by also analyzing COVID-19 vaccination data. Cornell University 2021-09-13 /pmc/articles/PMC8452099/ /pubmed/34545333 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Karr, Alan F. Hauzel, Jason Menon, Prahlad Porter, Adam A. Schaefer, Marcel Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly |
title | Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly |
title_full | Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly |
title_fullStr | Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly |
title_full_unstemmed | Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly |
title_short | Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly |
title_sort | specified certainty classification, with application to read classification for reference-guided metagenomic assembly |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452099/ https://www.ncbi.nlm.nih.gov/pubmed/34545333 |
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