Evidence map›Paper›PMID 38862018›Full record

ArticleOpen biology2024

Effective training of nanopore callers for epigenetic marks with limited labelled data.

Brian Yao, Chloe Hsu, Gal Goldner, Yael Michaeli, Yuval Ebenstein, Jennifer Listgarten

Abstract read
In one paragraph

Article in Open biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
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

6 authors.

Brian YaoDepartment of Electrical Engineering & Computer Sciences, University of California , Berkeley, CA 94720, USA.
Chloe HsuDepartment of Electrical Engineering & Computer Sciences, University of California , Berkeley, CA 94720, USA.
Gal GoldnerDepartment of Chemical Physics, Tel Aviv University , Tel Aviv-Yafo, Israel.
Yael MichaeliDepartment of Chemical Physics, Tel Aviv University , Tel Aviv-Yafo, Israel.
Yuval EbensteinDepartment of Chemical Physics, Tel Aviv University , Tel Aviv-Yafo, Israel.
Jennifer ListgartenDepartment of Electrical Engineering & Computer Sciences, University of California , Berkeley, CA 94720, USA.ORCID 0000-0002-6600-1431

Funding

European Research Council
6 · The paper itself

Abstract

Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5-methylcytosine (5mC) and N6-methyladenine (6mA). These ML-based nanopore callers have typically been trained on data that span all modifications on all possible DNA [Formula: see text]-mer backgrounds-a

Indexed as

5-MethylcytosineDNA MethylationEpigenesis, GeneticNeural Networks, ComputerDNAHumansMarkov ChainsNanoporesNanopore Sequencing5-MethylcytosineDNAepigeneticslimited training datananoporesupervised training

Identifiers

PMID38862018
PMCPMC11286150

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.