Evidence map›Paper›PMID 34790508›Full record

Article3 Biotech2021

Improved recognition of splice sites in

Prabina Kumar Meher, Subhrajit Satpathy

Open access · greenAbstract read
In one paragraph

Article in 3 Biotech, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 55% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

2 authors at 1 institution in 1 country.

Prabina Kumar MeherICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012 India.ORCID 0000-0002-7098-8785
Subhrajit SatpathyICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012 India.
Indian Agricultural Statistics Research Institute · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of splice sites is an important aspect with regard to the prediction of gene structure. In most of the existing splice site prediction studies, machine learning algorithms coupled with sequence-derived features have been successfully employed for splice site recognition. However, the splice site identification by incorporating the secondary structure information is lacking, particularly in plant species. Thus, we made an attempt in this study to evaluate the performance of structural features on the splice site prediction accuracy in SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-03036-8.

Indexed as

Computational biologyMachine learningNucleotide dependenciesSecondary structureSplice junction

Identifiers

PMID34790508
PMCPMC8558126
OpenAlexW3210404443

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.