Literature DB >> 24470573

Network orientation via shortest paths.

Dana Silverbush1, Roded Sharan.   

Abstract

UNLABELLED: The graph orientation problem calls for orienting the edges of a graph so as to maximize the number of pre-specified source-target vertex pairs that admit a directed path from the source to the target. Most algorithmic approaches to this problem share a common preprocessing step, in which the input graph is reduced to a tree by repeatedly contracting its cycles. Although this reduction is valid from an algorithmic perspective, the assignment of directions to the edges of the contracted cycles becomes arbitrary, and the connecting source-target paths may be arbitrarily long. In the context of biological networks, the connection of vertex pairs via shortest paths is highly motivated, leading to the following problem variant: given a graph and a collection of source-target vertex pairs, assign directions to the edges so as to maximize the number of pairs that are connected by a shortest (in the original graph) directed path. This problem is NP-complete and hard to approximate to within sub-polynomial factors. Here we provide a first polynomial-size integer linear program formulation for this problem, which allows its exact solution in seconds on current networks. We apply our algorithm to orient protein-protein interaction networks in yeast and compare it with two state-of-the-art algorithms. We find that our algorithm outperforms previous approaches and can orient considerable parts of the network, thus revealing its structure and function.
AVAILABILITY AND IMPLEMENTATION: The source code is available at www.cs.tau.ac.il/∼roded/shortest.zip. CONTACT: roded@post.tau.ac.il.

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Year:  2014        PMID: 24470573     DOI: 10.1093/bioinformatics/btu043

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  11 in total

Review 1.  An overview of bioinformatics methods for modeling biological pathways in yeast.

Authors:  Jie Hou; Lipi Acharya; Dongxiao Zhu; Jianlin Cheng
Journal:  Brief Funct Genomics       Date:  2015-10-17       Impact factor: 4.241

2.  Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways.

Authors:  Sadegh Azimzadeh Jamalkandi; Sayed-Hamidreza Mozhgani; Hamid Gholami Pourbadie; Mehdi Mirzaie; Farshid Noorbakhsh; Behrouz Vaziri; Alireza Gholami; Naser Ansari-Pour; Mohieddin Jafari
Journal:  Front Microbiol       Date:  2016-11-07       Impact factor: 5.640

3.  Reconstructing signaling pathways using regular language constrained paths.

Authors:  Mitchell J Wagner; Aditya Pratapa; T M Murali
Journal:  Bioinformatics       Date:  2019-07-15       Impact factor: 6.937

4.  An optimization framework for network annotation.

Authors:  Sushant Patkar; Roded Sharan
Journal:  Bioinformatics       Date:  2018-07-01       Impact factor: 6.937

5.  Myometrial Transcriptional Signatures of Human Parturition.

Authors:  Zachary Stanfield; Pei F Lai; Kaiyu Lei; Mark R Johnson; Andrew M Blanks; Roberto Romero; Mark R Chance; Sam Mesiano; Mehmet Koyutürk
Journal:  Front Genet       Date:  2019-04-01       Impact factor: 4.599

6.  A systematic approach to orient the human protein-protein interaction network.

Authors:  Dana Silverbush; Roded Sharan
Journal:  Nat Commun       Date:  2019-07-09       Impact factor: 14.919

7.  Inferring signalling dynamics by integrating interventional with observational data.

Authors:  Mathias Cardner; Nathalie Meyer-Schaller; Gerhard Christofori; Niko Beerenwinkel
Journal:  Bioinformatics       Date:  2019-07-15       Impact factor: 6.937

8.  Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family.

Authors:  Marina Uhart; Gabriel Flores; Diego M Bustos
Journal:  Sci Rep       Date:  2016-05-19       Impact factor: 4.379

9.  Pathways on demand: automated reconstruction of human signaling networks.

Authors:  Anna Ritz; Christopher L Poirel; Allison N Tegge; Nicholas Sharp; Kelsey Simmons; Allison Powell; Shiv D Kale; T M Murali
Journal:  NPJ Syst Biol Appl       Date:  2016-03-03

10.  Reconstructing cancer drug response networks using multitask learning.

Authors:  Matthew Ruffalo; Petar Stojanov; Venkata Krishna Pillutla; Rohan Varma; Ziv Bar-Joseph
Journal:  BMC Syst Biol       Date:  2017-10-10
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