Evidence map›Paper›PMID 35696082›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2022

iPTMnet RESTful API for Post-translational Modification Network Analysis.

Sachin Gavali, Karen E Ross, Julie Cowart, Chuming Chen, Cathy H Wu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2022. 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
–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

1 citing paper in PubMed.

  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

5 authors.

Sachin GavaliCenter for Bioinformatics and Computational Biology, Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA. saching@udel.edu.
Karen E RossDepartment of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, USA.
Julie CowartCenter for Bioinformatics and Computational Biology, Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
Chuming ChenCenter for Bioinformatics and Computational Biology, Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
Cathy H WuCenter for Bioinformatics and Computational Biology, Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

iPTMnet is a resource that combines rich information about protein post-translational modifications (PTM) from curated databases as well as text mining tools. Researchers can use the iPTMnet website to query, analyze and download the PTM data. In this chapter we describe the iPTMnet RESTful API which provides a way to streamline the integration of iPTMnet data into an automated data analysis workflow. In the first section, we give an overview of the architecture of the API. In the second section, we describe various function defined by the API and provide detailed examples of using these functions.

Indexed as

Data MiningProtein Processing, Post-TranslationalDatabases, ProteinProteinsWorkflowProteinsDatabasePhosphorylationPost-translational modificationProtein-protein interactionRESTful API

Identifiers

PMID35696082
PMCPMC10082948

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.