Evidence map›Paper›PMID 32597092›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2020

[Identification of nucleosome positioning using support vector machine method based on comprehensive DNA sequence feature].

Ying Cui, Zelong Xu, Jianzhong Li

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2020. 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, top 94% of its field
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

3 authors at 2 institutions in 1 country.

Ying CuiElectronic Engineering College, Heilongjiang University, Harbin 150080, P.R.China;School of Bioinformatics Sciences and Technology, Harbin Medical University, Harbin 150081, P.R.China.
Zelong XuSchool of Bioinformatics Sciences and Technology, Harbin Medical University, Harbin 150081, P.R.China.
Jianzhong LiElectronic Engineering College, Heilongjiang University, Harbin 150080, P.R.China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R.China.
Harbin Medical University · CNHarbin University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, based on z-curve theory and position weight matrix (PWM), a model for nucleosome sequences was constructed. Nucleosome sequence dataset was transformed into three-dimensional coordinates, PWM of the nucleosome sequences was calculated and the similarity score was obtained. After integrating them, a nucleosome feature model based on the comprehensive DNA sequences was obtained and named CSeqFM. We calculated the Euclidean distance between nucleosome sequence candidates or linker sequences and CSeqFM model as the feature dataset, and put the feature datasets into the support vector machine (SVM) for training and testing by ten-fold cross-validation. The results showed that the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of identifying nucleosome positioning for

Indexed as

DNANucleosomesSupport Vector MachineAnimalsCaenorhabditis elegansDrosophila melanogasterReproducibility of ResultsSaccharomyces cerevisiaeDNANucleosomeseuclidean distancenucleosomeposition weight matrixsequence featuresupport vector machinez-curve

Identifiers

PMID32597092
PMCPMC10319573
OpenAlexW3037967545

What OpenQuestion holds

Textmetadata
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.