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

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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