Literature DB >> 28062413

Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin.

Pietro Hiram Guzzi1, Tijana Milenkovic2.   

Abstract

Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology.

Mesh:

Substances:

Year:  2018        PMID: 28062413     DOI: 10.1093/bib/bbw132

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  11 in total

1.  From homogeneous to heterogeneous network alignment via colored graphlets.

Authors:  Shawn Gu; John Johnson; Fazle E Faisal; Tijana Milenković
Journal:  Sci Rep       Date:  2018-08-21       Impact factor: 4.379

2.  Pairwise Versus Multiple Global Network Alignment.

Authors:  Vipin Vijayan; Shawn Gu; Eric T Krebs; Lei Meng; Tijana MilenkoviĆ
Journal:  IEEE Access       Date:  2020-02-27       Impact factor: 3.367

3.  Data-driven network alignment.

Authors:  Shawn Gu; Tijana Milenković
Journal:  PLoS One       Date:  2020-07-02       Impact factor: 3.240

4.  An extensive assessment of network alignment algorithms for comparison of brain connectomes.

Authors:  Marianna Milano; Pietro Hiram Guzzi; Olga Tymofieva; Duan Xu; Christofer Hess; Pierangelo Veltri; Mario Cannataro
Journal:  BMC Bioinformatics       Date:  2017-06-06       Impact factor: 3.169

5.  A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product.

Authors:  Tie Shen; Zhengdong Zhang; Zhen Chen; Dagang Gu; Shen Liang; Yang Xu; Ruiyuan Li; Yimin Wei; Zhijie Liu; Yin Yi; Xiaoyao Xie
Journal:  Sci Rep       Date:  2018-11-06       Impact factor: 4.379

6.  Juxtapose: a gene-embedding approach for comparing co-expression networks.

Authors:  Katie Ovens; Farhad Maleki; B Frank Eames; Ian McQuillan
Journal:  BMC Bioinformatics       Date:  2021-03-16       Impact factor: 3.169

7.  Exploring the conservation of Alzheimer-related pathways between H. sapiens and C. elegans: a network alignment approach.

Authors:  Avgi E Apostolakou; Xhuliana K Sula; Katerina C Nastou; Georgia I Nasi; Vassiliki A Iconomidou
Journal:  Sci Rep       Date:  2021-02-25       Impact factor: 4.379

8.  Using dual-network-analyser for communities detecting in dual networks.

Authors:  Pietro Hiram Guzzi; Giuseppe Tradigo; Pierangelo Veltri
Journal:  BMC Bioinformatics       Date:  2022-01-10       Impact factor: 3.169

9.  Alignment of dynamic networks.

Authors:  V Vijayan; D Critchlow; T Milenkovic
Journal:  Bioinformatics       Date:  2017-07-15       Impact factor: 6.937

Review 10.  From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology.

Authors:  Maria Eugenia Gallo Cantafio; Katia Grillone; Daniele Caracciolo; Francesca Scionti; Mariamena Arbitrio; Vito Barbieri; Licia Pensabene; Pietro Hiram Guzzi; Maria Teresa Di Martino
Journal:  High Throughput       Date:  2018-10-26
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