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Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function

Authors:

Tatsuya Akutsu,

Satoru KuharaAuthors Info & Claims

RECOMB '00: Proceedings of the fourth annual international conference on Computational molecular biology

Pages 8 - 14

https://doi.org/10.1145/332306.332317

Published: 08 April 2000 Publication History

Abstract

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology Several studies have been done using the Boolean network as a model of a genetic network This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mn^D+1) time naive algorithm and a simple O(mn^D) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum indegree. This paper presents an improved O(m^w-2n^D + mn^D+w-3) time Monte-Carlo type randomized algorithm, where w is the exponent of matrix multiplication (currently, w < 2376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is non-trivial and the technique can be applied to several related problems.

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Cited By

Guan QDeBardeleben NBlanchard SFu SDavis IV CJones W(2016)Analyzing the Robustness of HPC Applications Using a Fine-Grained Soft Error Fault Injection ToolInnovative Research and Applications in Next-Generation High Performance Computing10.4018/978-1-5225-0287-6.ch011(277-305)Online publication date: 2016
https://doi.org/10.4018/978-1-5225-0287-6.ch011
Čepek OKronus DKučera P(2010)Analysing DNA microarray data using Boolean techniquesAnnals of Operations Research10.1007/s10479-010-0723-0188:1(77-110)Online publication date: 24-Feb-2010
https://doi.org/10.1007/s10479-010-0723-0
Yeh HCheng SLin YYeh CLin SSoo V(2009)Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independencyBMC Medical Genomics10.1186/1755-8794-2-702:1Online publication date: 21-Dec-2009
https://doi.org/10.1186/1755-8794-2-70
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Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function

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Published In

cover image ACM Conferences

RECOMB '00: Proceedings of the fourth annual international conference on Computational molecular biology

April 2000

329 pages

ISBN:1581131860

DOI:10.1145/332306

Editors:
Ron Shamir
Tel-Aviv Univ., Israel
,
Satoru Miyano
Univ. of Tokyo, Tokyo, Japan
,
Sorin Istrail
Sandia National Labs
,
Pavel Pevzner
Univ. of Southern California
,
Michael Waterman
Univ. of Southern California

Copyright © 2000 ACM.

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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Published: 08 April 2000

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Cited By

Guan QDeBardeleben NBlanchard SFu SDavis IV CJones W(2016)Analyzing the Robustness of HPC Applications Using a Fine-Grained Soft Error Fault Injection ToolInnovative Research and Applications in Next-Generation High Performance Computing10.4018/978-1-5225-0287-6.ch011(277-305)Online publication date: 2016
https://doi.org/10.4018/978-1-5225-0287-6.ch011
Čepek OKronus DKučera P(2010)Analysing DNA microarray data using Boolean techniquesAnnals of Operations Research10.1007/s10479-010-0723-0188:1(77-110)Online publication date: 24-Feb-2010
https://doi.org/10.1007/s10479-010-0723-0
Yeh HCheng SLin YYeh CLin SSoo V(2009)Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independencyBMC Medical Genomics10.1186/1755-8794-2-702:1Online publication date: 21-Dec-2009
https://doi.org/10.1186/1755-8794-2-70
Abul OAlhajj RPolat F(2006)Asymptotical lower limits on required number of examples for learning boolean networksProceedings of the 21st international conference on Computer and Information Sciences10.1007/11902140_18(154-164)Online publication date: 1-Nov-2006
https://dl.acm.org/doi/10.1007/11902140_18
Spieth CStreichert FSpeer NZell A(2005)Multi-objective model optimization for inferring gene regulatory networksProceedings of the Third international conference on Evolutionary Multi-Criterion Optimization10.1007/978-3-540-31880-4_42(607-620)Online publication date: 9-Mar-2005
https://dl.acm.org/doi/10.1007/978-3-540-31880-4_42
Spieth CStreichert FSpeer NZell A(2004)A memetic inference method for gene regulatory networks based on S-SystemsProceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)10.1109/CEC.2004.1330851(152-157)Online publication date: 2004
https://doi.org/10.1109/CEC.2004.1330851
Spieth CStreichert FSpeer NZell A(2004)Utilizing an island model for EA to preserve solution diversity for inferring gene regulatory networksProceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)10.1109/CEC.2004.1330850(146-151)Online publication date: 2004
https://doi.org/10.1109/CEC.2004.1330850
Abul OAlhaj RPolat F(2004)Markov decision processes based optimal control policies for probabilistic boolean networksProceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering10.1109/BIBE.2004.1317363(337-344)Online publication date: 2004
https://doi.org/10.1109/BIBE.2004.1317363
Cotta CTroya J(2004)Reverse engineering of temporal Boolean networks from noisy data using evolutionary algorithmsNeurocomputing10.1016/j.neucom.2003.12.00762:C(111-129)Online publication date: 1-Dec-2004
https://dl.acm.org/doi/10.1016/j.neucom.2003.12.007
Spieth CStreichert FSpeer NZell A(2004)Iteratively Inferring Gene Regulatory Networks with Virtual Knockout ExperimentsApplications of Evolutionary Computing10.1007/978-3-540-24653-4_11(104-112)Online publication date: 2004
https://doi.org/10.1007/978-3-540-24653-4_11
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