Evidence map›Paper›PMID 32517331›Full record

ArticleBiomolecules2020

PSIONplus

Jianzhao Gao, Hong Wei, Alberto Cano, Lukasz Kurgan

Open access · goldAbstract read
In one paragraph

Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023
    Review
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

4 authors at 2 institutions in 3 countries.

Jianzhao GaoSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
Hong WeiSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
Alberto CanoDepartment of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.ORCID 0000-0001-9027-298X
Lukasz KurganDepartment of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.ORCID 0000-0002-7749-0314
Nankai University · CNVirginia Commonwealth University · US

Funding

Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin KLMDASRNational Natural Science Foundation of China 11701296Natural Science Foundation of Tianjin 18JCQNJC09600
6 · The paper itself

Abstract

Computational prediction of ion channels facilitates the identification of putative ion channels from protein sequences. Several predictors of ion channels and their types were developed in the last quindecennial. While they offer reasonably accurate predictions, they also suffer a few shortcomings including lack of availability, parallel prediction mode, single-label prediction (inability to predict multiple channel subtypes), and incomplete scope (inability to predict subtypes of the voltage-gated channels). We developed a first-of-its-kind PSIONplus

Indexed as

AlgorithmsComputational BiologySoftwareIon ChannelsIon Channelsion channelion channel typeligand-gated ion channelmulti-label predictionsequential predictionvoltage-gated ion channel

Identifiers

PMID32517331
PMCPMC7355608
OpenAlexW3033505940

What Socratic 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 Socratic graph.