Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters

Amina Houari; Sadok Ben Yahia

doi:10.1007/978-3-030-78428-7_13

Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters

Amina Houari^*
, Sadok Ben Yahia

^*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

Abstract

Formal Concept Analysis has been widely applied to identify differently expressed genes among microarray data. Top-K Formal Concepts are identified as efficient in generating most important Formal Concepts. To the best of our knowledge, no currently available algorithm is able to perform this challenging task. Therefore, we introduce Top-BicMiner, a new method for mining biclusters from gene expression data through Top-k Formal Concepts. It performs the extraction of the sets of both positive and negative correlations biclusters. Top-BicMiner relies on Formal concept analysis as well as a specific discretization method. Extensive experiments, carried out on real-life datasets, shed light on Top-BicMiner’s ability to identify statistically and biologically significant biclusters.

Originalsprog	Engelsk
Titel	Model and Data Engineering - 10th International Conference, MEDI 2021, Proceedings
Redaktører	Christian Attiogbé, Sadok Ben Yahia
Forlag	Springer Science+Business Media
Publikationsdato	2021
Sider	156-172
ISBN (Trykt)	9783030784270
DOI	https://doi.org/10.1007/978-3-030-78428-7_13
Status	Udgivet - 2021
Udgivet eksternt	Ja
Begivenhed	10th International Conference on Model and Data Engineering, MEDI 2021 - Virtual, Online Varighed: 21. jun. 2021 → 23. jun. 2021

Konference

Konference	10th International Conference on Model and Data Engineering, MEDI 2021
By	Virtual, Online
Periode	21/06/2021 → 23/06/2021

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	12732 LNCS
ISSN	0302-9743

Bibliografisk note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Dokumenter og links

10.1007/978-3-030-78428-7_13

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

Houari, Amina ; Ben Yahia, Sadok. / Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters. Model and Data Engineering - 10th International Conference, MEDI 2021, Proceedings. red. / Christian Attiogbé ; Sadok Ben Yahia. Springer Science+Business Media, 2021. s. 156-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12732 LNCS).

@inproceedings{9801f5dc745b467e8bb1614eaee7a450,

title = "Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters",

abstract = "Formal Concept Analysis has been widely applied to identify differently expressed genes among microarray data. Top-K Formal Concepts are identified as efficient in generating most important Formal Concepts. To the best of our knowledge, no currently available algorithm is able to perform this challenging task. Therefore, we introduce Top-BicMiner, a new method for mining biclusters from gene expression data through Top-k Formal Concepts. It performs the extraction of the sets of both positive and negative correlations biclusters. Top-BicMiner relies on Formal concept analysis as well as a specific discretization method. Extensive experiments, carried out on real-life datasets, shed light on Top-BicMiner{\textquoteright}s ability to identify statistically and biologically significant biclusters.",

keywords = "Biclustering, Bioinformatics, DNA microarray data, Formal Concept Analysis, Negative correlation, Positive correlation, Top-K Formal Concepts",

author = "Amina Houari and \{Ben Yahia\}, Sadok",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 10th International Conference on Model and Data Engineering, MEDI 2021 ; Conference date: 21-06-2021 Through 23-06-2021",

year = "2021",

doi = "10.1007/978-3-030-78428-7\_13",

language = "English",

isbn = "9783030784270",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science+Business Media",

pages = "156--172",

editor = "Christian Attiogb{\'e} and \{Ben Yahia\}, Sadok",

booktitle = "Model and Data Engineering - 10th International Conference, MEDI 2021, Proceedings",

address = "United States",

}

Houari, A & Ben Yahia, S 2021, Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters. i C Attiogbé & S Ben Yahia (red), Model and Data Engineering - 10th International Conference, MEDI 2021, Proceedings. Springer Science+Business Media, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 12732 LNCS, s. 156-172, 10th International Conference on Model and Data Engineering, MEDI 2021, Virtual, Online, 21/06/2021. https://doi.org/10.1007/978-3-030-78428-7_13

Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters. / Houari, Amina; Ben Yahia, Sadok.
Model and Data Engineering - 10th International Conference, MEDI 2021, Proceedings. red. / Christian Attiogbé; Sadok Ben Yahia. Springer Science+Business Media, 2021. s. 156-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12732 LNCS).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters

AU - Houari, Amina

AU - Ben Yahia, Sadok

PY - 2021

Y1 - 2021

N2 - Formal Concept Analysis has been widely applied to identify differently expressed genes among microarray data. Top-K Formal Concepts are identified as efficient in generating most important Formal Concepts. To the best of our knowledge, no currently available algorithm is able to perform this challenging task. Therefore, we introduce Top-BicMiner, a new method for mining biclusters from gene expression data through Top-k Formal Concepts. It performs the extraction of the sets of both positive and negative correlations biclusters. Top-BicMiner relies on Formal concept analysis as well as a specific discretization method. Extensive experiments, carried out on real-life datasets, shed light on Top-BicMiner’s ability to identify statistically and biologically significant biclusters.

AB - Formal Concept Analysis has been widely applied to identify differently expressed genes among microarray data. Top-K Formal Concepts are identified as efficient in generating most important Formal Concepts. To the best of our knowledge, no currently available algorithm is able to perform this challenging task. Therefore, we introduce Top-BicMiner, a new method for mining biclusters from gene expression data through Top-k Formal Concepts. It performs the extraction of the sets of both positive and negative correlations biclusters. Top-BicMiner relies on Formal concept analysis as well as a specific discretization method. Extensive experiments, carried out on real-life datasets, shed light on Top-BicMiner’s ability to identify statistically and biologically significant biclusters.

KW - Biclustering

KW - Bioinformatics

KW - DNA microarray data

KW - Formal Concept Analysis

KW - Negative correlation

KW - Positive correlation

KW - Top-K Formal Concepts

U2 - 10.1007/978-3-030-78428-7_13

DO - 10.1007/978-3-030-78428-7_13

M3 - Article in proceedings

AN - SCOPUS:85111353506

SN - 9783030784270

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

SP - 156

EP - 172

BT - Model and Data Engineering - 10th International Conference, MEDI 2021, Proceedings

A2 - Attiogbé, Christian

A2 - Ben Yahia, Sadok

PB - Springer Science+Business Media

T2 - 10th International Conference on Model and Data Engineering, MEDI 2021

Y2 - 21 June 2021 through 23 June 2021

ER -

Houari A, Ben Yahia S. Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters. I Attiogbé C, Ben Yahia S, red., Model and Data Engineering - 10th International Conference, MEDI 2021, Proceedings. Springer Science+Business Media. 2021. s. 156-172. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12732 LNCS). doi: 10.1007/978-3-030-78428-7_13

Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters

Abstract

Konference

Bibliografisk note

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater