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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2024

12.09.2023 | Original Article

A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray data

verfasst von: Amina Houari, Sadok Ben Yahia

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2024

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Abstract

Analyzing and understanding large and complex volumes of biological data is a challenging task as data becomes more widely available. In biomedical research, gene expression data are among the most commonly used biological data. Formal concept analysis frequently identifies deferentially expressed genes in microarray data. Top-K formal concepts are effective at producing effective Formal Concepts. To our knowledge, no existing algorithm can complete the difficult task of identifying only important biclusters. For this purpose, a new Top-K formal concepts-based algorithm for mining biclusters from gene expression data is proposed: Top-BicMiner. It extracts biclusters’ sets with positively and negatively correlated genes according to distinct correlation measures. The proposed method is applied to both synthetic and real-life microarray datasets. The experimental results highlight the Top-BicMiner’s ability to identify statistically and biologically significant biclusters.
Vorheriger Artikel FF-GLAM-cs: a fusion framework based on GLAM with channel shuffle for speech emotion recognition
Nächster Artikel Segmentation-based context-aware enhancement network for medical images

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Fußnoten
1
TOPSIS stands for Order of Preference by Similarity to Ideal Solution [36].
 
2
We use a separator-free abbreviated form for the sets, e.g., \(\{I_{1}I_{2}I_{3}\}\) stands for the set of items \(\{I_1, I_2, I_3\}\).
 
3
The extraction of the formal concepts is carried out through the invocation of the efficient LCM algorithm [72].
 
4
nb1 is the number of FCs extracted in Line 9 and nb2 is the number of FCs extracted in Line 10(i.e \(nb=max(nb1,nb2\))).
 
10
The best biclusters have an adjusted p-value less than 0.001%.
 
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Metadaten
Titel
A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray data
verfasst von
Amina Houari
Sadok Ben Yahia
Publikationsdatum
12.09.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2024
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01949-9

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