Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly.

Alan F Karr, Jason Hauzel, Prahlad Menon, Adam A Porter, Marcel Schaefer

Open access · greenAbstract readPreprint

In one paragraph

Article in ArXiv, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Alan F KarrCenter Mid-Atlantic Fraunhofer USA, Riverdale, MD.

Jason HauzelCenter Mid-Atlantic Fraunhofer USA, Riverdale, MD.

Prahlad MenonCenter Mid-Atlantic Fraunhofer USA, Riverdale, MD.

Adam A PorterDepartment of Computer Science, University of Maryland, College Park, MD.

Marcel SchaeferCenter Mid-Atlantic Fraunhofer USA, Riverdale, MD.

University of Maryland, College Park · US

Funding

Algorithms and Software for the Assembly of Metagenomic DataR01AI100947 · NIAID · UNIV OF MARYLAND, COLLEGE PARK · PI POP, MIHAI · 2013 to 2025

$7.4M

NIAID NIH HHS R01 AI100947

6 · The paper itself

Abstract

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.

Indexed as

Bayesian analysisclassifiermetagenomicsposterior probabilitiesread classificationuncertainty quantification

Identifiers

PMID34545333

PMCPMC8452099

OpenAlexW3199336514

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.