ISaaC

Identifying structural relations in biological data with copula-based kernel dependency measures

Hossam Al Meer, RaghvenPhDa Mall, Ehsan Ullah, Nasreddine Megrez, Halima Bensmail

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The goal of this paper is to develop a novel statistical framework for inferring dependence between distributions of variables in omics data. We propose the concept of building a dependence network using a copula-based kernel dependency measures to reconstruct the underlying association network between the distributions. ISaaC is utilized for reverse-engineering gene regulatory networks and is competitive with several state-of-the-art gene regulatory inferrence methods on DREAM3 and DREAM4 Challenge datasets. An open-source implementation of ISaaC is available at https://bitbucket.org/HossamAlmeer/isaac/.

Original languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings
PublisherSpringer Verlag
Pages71-82
Number of pages12
ISBN (Print)9783319787220
DOIs
Publication statusPublished - 1 Jan 2018
Event6th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2018 - Granada, Spain
Duration: 25 Apr 201827 Apr 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10813 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2018
CountrySpain
CityGranada
Period25/4/1827/4/18

Fingerprint

Copula
Genes
kernel
Reverse engineering
Reverse Engineering
Gene Regulatory Network
Open Source
Gene
Concepts
Framework

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Al Meer, H., Mall, R., Ullah, E., Megrez, N., & Bensmail, H. (2018). ISaaC: Identifying structural relations in biological data with copula-based kernel dependency measures. In Bioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings (pp. 71-82). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10813 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-319-78723-7_6

ISaaC : Identifying structural relations in biological data with copula-based kernel dependency measures. / Al Meer, Hossam; Mall, RaghvenPhDa; Ullah, Ehsan; Megrez, Nasreddine; Bensmail, Halima.

Bioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings. Springer Verlag, 2018. p. 71-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10813 LNBI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Al Meer, H, Mall, R, Ullah, E, Megrez, N & Bensmail, H 2018, ISaaC: Identifying structural relations in biological data with copula-based kernel dependency measures. in Bioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10813 LNBI, Springer Verlag, pp. 71-82, 6th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2018, Granada, Spain, 25/4/18. https://doi.org/10.1007/978-3-319-78723-7_6
Al Meer H, Mall R, Ullah E, Megrez N, Bensmail H. ISaaC: Identifying structural relations in biological data with copula-based kernel dependency measures. In Bioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings. Springer Verlag. 2018. p. 71-82. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-78723-7_6
Al Meer, Hossam ; Mall, RaghvenPhDa ; Ullah, Ehsan ; Megrez, Nasreddine ; Bensmail, Halima. / ISaaC : Identifying structural relations in biological data with copula-based kernel dependency measures. Bioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings. Springer Verlag, 2018. pp. 71-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{c104852b253b42f1bef6519eaf37ded1,
title = "ISaaC: Identifying structural relations in biological data with copula-based kernel dependency measures",
abstract = "The goal of this paper is to develop a novel statistical framework for inferring dependence between distributions of variables in omics data. We propose the concept of building a dependence network using a copula-based kernel dependency measures to reconstruct the underlying association network between the distributions. ISaaC is utilized for reverse-engineering gene regulatory networks and is competitive with several state-of-the-art gene regulatory inferrence methods on DREAM3 and DREAM4 Challenge datasets. An open-source implementation of ISaaC is available at https://bitbucket.org/HossamAlmeer/isaac/.",
author = "{Al Meer}, Hossam and RaghvenPhDa Mall and Ehsan Ullah and Nasreddine Megrez and Halima Bensmail",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-78723-7_6",
language = "English",
isbn = "9783319787220",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "71--82",
booktitle = "Bioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings",

}

TY - GEN

T1 - ISaaC

T2 - Identifying structural relations in biological data with copula-based kernel dependency measures

AU - Al Meer, Hossam

AU - Mall, RaghvenPhDa

AU - Ullah, Ehsan

AU - Megrez, Nasreddine

AU - Bensmail, Halima

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The goal of this paper is to develop a novel statistical framework for inferring dependence between distributions of variables in omics data. We propose the concept of building a dependence network using a copula-based kernel dependency measures to reconstruct the underlying association network between the distributions. ISaaC is utilized for reverse-engineering gene regulatory networks and is competitive with several state-of-the-art gene regulatory inferrence methods on DREAM3 and DREAM4 Challenge datasets. An open-source implementation of ISaaC is available at https://bitbucket.org/HossamAlmeer/isaac/.

AB - The goal of this paper is to develop a novel statistical framework for inferring dependence between distributions of variables in omics data. We propose the concept of building a dependence network using a copula-based kernel dependency measures to reconstruct the underlying association network between the distributions. ISaaC is utilized for reverse-engineering gene regulatory networks and is competitive with several state-of-the-art gene regulatory inferrence methods on DREAM3 and DREAM4 Challenge datasets. An open-source implementation of ISaaC is available at https://bitbucket.org/HossamAlmeer/isaac/.

UR - http://www.scopus.com/inward/record.url?scp=85045938440&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045938440&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-78723-7_6

DO - 10.1007/978-3-319-78723-7_6

M3 - Conference contribution

SN - 9783319787220

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 71

EP - 82

BT - Bioinformatics and Biomedical Engineering - 6th International Work-Conference, IWBBIO 2018, Proceedings

PB - Springer Verlag

ER -