Evidence map›Paper›PMID 30066633›Full record

ArticleBMC bioinformatics2018

NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability.

Krishna Kanhaiya, Vladimir Rogojin, Keivan Kazemi, Eugen Czeizler, Ion Petre

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. 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.

Krishna KanhaiyaComputational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.
Vladimir RogojinComputational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.
Keivan KazemiComputational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.
Eugen CzeizlerComputational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.
Ion PetreComputational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland. ipetre@abo.fi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNetwork controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer.

resultWe develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user's query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php .

conclusionThe pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.

Indexed as

SoftwareAlgorithmsComputational BiologyDatabases, FactualGene Regulatory NetworksHumansCancerData acquisition and integrationNetwork controllabilityPersonalized medicineProtein-protein interaction networksSoftware pipelineWeb service

Identifiers

PMID30066633
PMCPMC6069765

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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.