Evidence map›Paper›PMID 34415996›Full record

ArticleDatabase : the journal of biological databases and curation2021

LINPS: a database for cancer-cell-specific perturbations of biological networks.

Mahmoud Ahmed, Deok Ryong Kim

Open access · goldAbstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2021. 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
0.2field-weighted citation impact, top 51% 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, 2 citations in OpenAlex.

  1. Article
  2. Maximizing the utility of public data.Frontiers in genetics · 2023
    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.

Mahmoud AhmedDepartment of Biochemistry and Convergence Medical Science, Institute of Health Sciences, Gyeongsang National University College of Medicine, 816 Beon-gil 15, Jinju-daero, Jinju 52727, South Korea.ORCID 0000-0002-4377-6541
Deok Ryong KimDepartment of Biochemistry and Convergence Medical Science, Institute of Health Sciences, Gyeongsang National University College of Medicine, 816 Beon-gil 15, Jinju-daero, Jinju 52727, South Korea.ORCID 0000-0002-3288-8257
Gyeongsang National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Screening for potential cancer therapies using existing large datasets of drug perturbations requires expertise and resources not available to all. This is often a barrier for lab scientists to tap into these valuable resources. To address these issues, one can take advantage of prior knowledge especially those coded in standard formats such as causal biological networks (CBN). Large datasets can be converted into appropriate structures, analyzed once and the results made freely available in easy-to-use formats. We used the Library of Integrated Cellular Signatures to model the cell-specific effect of hundreds of drug treatments on gene expression. These signatures were then used to predict the effect of the treatments on several CBN using the network perturbation amplitudes analysis. We packaged the pre-computed scores in a database with an interactive web interface. The intuitive user-friendly interface can be used to query the database for drug perturbations and quantify their effect on multiple key biological functions in cancer cell lines. In addition to describing the process of building the database and the interface, we provide a realistic use case to explain how to use and interpret the results. To sum, we pre-computed cancer-cell-specific perturbation amplitudes of several biological networks and made the output available in a database with an interactive web interface. Database URL https://mahshaaban.shinyapps.io/LINPSAPP/.

Indexed as

NeoplasmsDatabases, FactualGene Expression

Identifiers

PMID34415996
PMCPMC8378515
OpenAlexW3195695225

What OpenQuestion 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 OpenQuestion graph.