NemoProfile as an efficient approach to network motif analysis with instance collection.

Wooyoung Kim, Lynnette Haukap

Abstract read

In one paragraph

Article in BMC bioinformatics, 2017. 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

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3 · Its place in the literature

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1 citing paper in PubMed.

Integrating protein interaction and pathway crosstalk network reveals a promising therapeutic approach for psoriasis through apoptosis induction.Scientific reports · 2024
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4 · The record

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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.

Wooyoung KimDivision of Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics (STEM), University of Washington Bothell, 18115 Campus Way NE, Bothell, 98011-8246, WA, USA. kimw6@uw.edu.

Lynnette HaukapDivision of Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics (STEM), University of Washington Bothell, 18115 Campus Way NE, Bothell, 98011-8246, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA network motif is defined as a statistically significant and recurring subgraph pattern within a network. Most existing instance collection methods are not feasible due to high memory usage issues and provision of limited network motif information. They require a two-step process that requires network motif identification prior to instance collection. Due to the impracticality in obtaining motif instances, the significance of their contribution to problem solving is debated within the field of biology.

resultsThis paper presents NemoProfile, an efficient new network motif data model. NemoProfile simplifies instance collection by resolving memory overhead issues and is seamlessly generated, thus eliminating the need for costly two-step processing. Additionally, a case study was conducted to demonstrate the application of network motifs to existing problems in the field of biology.

conclusionNemoProfile comprises network motifs and their instances, thereby facilitating network motifs usage in real biological problems.

Indexed as

AlgorithmsGene Regulatory NetworksComputational BiologyProtein Interaction MapsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSaccharomyces cerevisiae ProteinsBiological networkEssential proteinESUNemoCollectNemoProfileNetwork motifSystems biology

Identifiers

PMID29072139

DOI10.1186/s12859-017-1822-6

PMCPMC5657038

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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.