research-article

Co-evolving Patterns in Temporal Networks of Varying Evolution

Authors:

Aisharjya Sarkar,

Pietro Cinaglia,

Christina Boucher,

Tamer KahveciAuthors Info & Claims

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 494 - 503

https://doi.org/10.1145/3307339.3342152

Published: 04 September 2019 Publication History

Abstract

Biological networks describe the interactions among molecules. Unlike static biological networks at a single time point, temporal networks capture how the network topology evolves over time in response to external stimuli or internal variations. We say that two temporal networks have co-evolving subnetworks if the topologies of these subnetworks remain similar to each other as the networks evolve over time. Existing methods for identifying co-evolving patterns make the strong and unrealistic assumption that the two network topologies evolve at the same rate. In this paper, we consider the generalized problem, where each network may evolve at different rates and the rates of evolution may change over time. Moreover, the two networks may have network topologies available for different number of time points. Existing methods fail to solve this problem as they rely on the strong prior assumption. We develop an efficient algorithm, Tempo++, for identifying co-evolving subnetworks within two given temporal networks. Unlike existing methods, Tempo++ does not assume that the networks have same and uniform evolutionary rates. We experimentally demonstrate that Tempo++ scales efficiently and accurately on both synthetic and real datasets. Our results on E. coli time resolved response to five different environmental stress conditions demonstrate that Tempo++ identifies genes specific to those conditions that conforms to well-known studies in the literature. Statistical significance of alignments found by Tempo++ outperforms existing strategies. Moreover, Tempo++ correctly identifies co-evolving networks with similar stress response compared to networks with different stress response.

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Alfano CFarina LPetti M(2023)Networks as Biomarkers: Uses and PurposesGenes10.3390/genes1402042914:2(429)Online publication date: 8-Feb-2023
https://doi.org/10.3390/genes14020429
Cinaglia PCannataro M(2023)Identifying Candidate Gene–Disease Associations via Graph Neural NetworksEntropy10.3390/e2506090925:6(909)Online publication date: 7-Jun-2023
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Index Terms

Co-evolving Patterns in Temporal Networks of Varying Evolution
1. Networks
  1. Network algorithms
  2. Network properties
    1. Network reliability

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cover image ACM Conferences

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 2019

716 pages

ISBN:9781450366663

DOI:10.1145/3307339

General Chairs:
Xinghua (Mindy) Shi
Temple University, USA
,
Michael Buck
University of Buffalo, USA
,
Program Chairs:
Jian Ma
Carnegie Mellon University, USA
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Pierangelo Veltri
University Magna Graecia of Catanzaro, Italy

Copyright © 2019 ACM.

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Published: 04 September 2019

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September 7 - 10, 2019

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Cited By

Milano MCinaglia PGuzzi PCannataro M(2023)Aligning Cross-Species Interactomes for Studying Complex and Chronic DiseasesLife10.3390/life1307152013:7(1520)Online publication date: 6-Jul-2023
https://doi.org/10.3390/life13071520
Alfano CFarina LPetti M(2023)Networks as Biomarkers: Uses and PurposesGenes10.3390/genes1402042914:2(429)Online publication date: 8-Feb-2023
https://doi.org/10.3390/genes14020429
Cinaglia PCannataro M(2023)Identifying Candidate Gene–Disease Associations via Graph Neural NetworksEntropy10.3390/e2506090925:6(909)Online publication date: 7-Jun-2023
https://doi.org/10.3390/e25060909
Cinaglia PCannataro M(2023)A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic NetworksEntropy10.3390/e2504066525:4(665)Online publication date: 15-Apr-2023
https://doi.org/10.3390/e25040665
Qing H(2023)Estimating the Number of Communities in Weighted NetworksEntropy10.3390/e2504055125:4(551)Online publication date: 23-Mar-2023
https://doi.org/10.3390/e25040551
Cinaglia PVázquez-Poletti JCannataro M(2023)Massive Parallel Alignment of RNA-seq Reads in Serverless ComputingBig Data and Cognitive Computing10.3390/bdcc70200987:2(98)Online publication date: 15-May-2023
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Cinaglia PMilano MCannataro M(2023)Multilayer network alignment based on topological assessment via embeddingsBMC Bioinformatics10.1186/s12859-023-05508-524:1Online publication date: 6-Nov-2023
https://doi.org/10.1186/s12859-023-05508-5
Wen JGabrys BMusial K(2023)Review and Assessment of Digital Twin–Oriented Social Network SimulatorsIEEE Access10.1109/ACCESS.2023.331212911(97503-97521)Online publication date: 2023
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Cinaglia PVazquez-Poletti JCannataro M(2022)Serverless computing for RNA-Seq data analysis2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995566(2175-2181)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995566
Cinaglia PCannataro M(2022)Alignment of Dynamic Networks based on Temporal Embeddings2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9994863(2511-2518)Online publication date: 6-Dec-2022
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