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Biclustering of time series data using factor graphs

Published: 03 April 2017 Publication History

Abstract

Biclustering regards the simultaneous clustering of both rows and columns of a given data matrix. A specific application scenario for biclustering techniques concerns the analysis of gene expression time-series data, wherein columns dataset are temporally related. In this context, biclustering solutions should involve subset of genes sharing 'similar' behaviours among consecutive experimental conditions. Due to the intrinsic spatial constraint required by time-series dataset, current Factor Graph (FG) based approaches cannot be applied. In this paper we introduce Time-Series constraints forcing biclustering solution to have contiguous columns. We optimize the model by using the Max-Sum algorithm, whose message update rules have been derived exploiting The Higher Order Potentials (THOP). The proposed method has been assessed on a real world dataset and the retrieved biclusters show that it can provide accurate and biologically relevant solutions.

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Cited By

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  • (2022)Water Consumption Pattern Analysis Using Biclustering: When, Why and HowWater10.3390/w1412195414:12(1954)Online publication date: 18-Jun-2022
  • (2019)Biclustering of Smart Building Electric Energy Consumption DataApplied Sciences10.3390/app90202229:2(222)Online publication date: 9-Jan-2019
  • (2019)Subspace clustering for situation assessment in aquatic dronesProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297372(930-937)Online publication date: 8-Apr-2019

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  1. Biclustering of time series data using factor graphs

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    cover image ACM Conferences
    SAC '17: Proceedings of the Symposium on Applied Computing
    April 2017
    2004 pages
    ISBN:9781450344869
    DOI:10.1145/3019612
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 03 April 2017

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    Author Tags

    1. biclustering
    2. factor graph
    3. gene expression
    4. max-sum
    5. time series

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    SAC 2017: Symposium on Applied Computing
    April 3 - 7, 2017
    Marrakech, Morocco

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    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    Cited By

    View all
    • (2022)Water Consumption Pattern Analysis Using Biclustering: When, Why and HowWater10.3390/w1412195414:12(1954)Online publication date: 18-Jun-2022
    • (2019)Biclustering of Smart Building Electric Energy Consumption DataApplied Sciences10.3390/app90202229:2(222)Online publication date: 9-Jan-2019
    • (2019)Subspace clustering for situation assessment in aquatic dronesProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297372(930-937)Online publication date: 8-Apr-2019

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