Evidence map›Paper›PMID 34270057›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2021

Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.

Min Zhang, Guangyou Duan

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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
1.0field-weighted citation impact, top 25% 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, 2 citations in OpenAlex.

  1. 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

2 authors at 2 institutions in 1 country.

Min ZhangDepartment of Neurology, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.
Guangyou DuanEnergy Plant Research Center, School of Life Sciences, Qilu Normal University, Jinan, People's Republic of China. guangyou.duan@qlnu.edu.cn.
Qilu Normal University · CNShandong First Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein kinase and phosphatase, respectively, constitutes a key mechanism of molecular information flow in cellular systems. The protein interactions of kinases, phosphatases, and their regulatory subunits and substrates are the main part of phosphorylation networks. To elucidate the landscape of phosphorylation events has been a central goal pursued by both experimental and computational approaches. Substrate specificity (e.g., sequence, structure) or the phosphoproteome has been utilized in an array of different statistical learning methods to infer phosphorylation networks. In this chapter, different computational phosphorylation network inference-related methods and resources are summarized and discussed.

Indexed as

Neural Networks, ComputerProtein Processing, Post-TranslationalPhosphoric Monoester HydrolasesPhosphorylationProtein KinasesProteomeSubstrate SpecificityPhosphoric Monoester HydrolasesProtein KinasesProteomeKinase–substrate relationshipNetwork reconstructionPhosphatase–substrate relationshipPhosphorylation networkPosttranslational modificationProtein–protein interaction

Identifiers

PMID34270057
OpenAlexW3178559546

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.