2025

Conférence

IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI)

Biclustering is an important data analysis technique
that simultaneously groups coherent rows and columns of a
matrix. It has broad applications, especially in bioinformatics,
where it helps identify groups of genes with similar behavior under
specific experimental conditions, potentially revealing shared
biological functions. Although many algorithms have been developed,
including evolutionary approaches, most rely on statistical
criteria that may not reflect true biological significance, making
interpretation difficult for biologists. To address this limitation,
we propose integrating biological knowledge directly into an
evolutionary algorithm, particularly during the selection and
crossover phases. We also employ a global alignment technique
with a binary mask to guide crossover operations more effectively.
Experiments conducted on real Saccharomyces cerevisiae data
confirm that integrating biological knowledge yields biologically
meaningful and non-trivial biclusters, demonstrating the effectiveness
of our approach.

@INPROCEEDINGS {11272794,
author = { Chedly, Jihen and Maatouk, Ons and Ayadi, Wassim },
booktitle = { 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI) },
title = {{ Biological Knowledge-Driven Evolutionary Algorithm for Biclustering Gene Expression Data }},
year = {2025},
volume = {},
ISSN = {},
pages = {705-709},
abstract = { Biclustering is an important data analysis technique that simultaneously groups coherent rows and columns of a matrix. It has broad applications, especially in bioinformatics, where it helps identify groups of genes with similar behavior under specific experimental conditions, potentially revealing shared biological functions. Although many algorithms have been developed, including evolutionary approaches, most rely on statistical criteria that may not reflect true biological significance, making interpretation difficult for biologists. To address this limitation, we propose integrating biological knowledge directly into an evolutionary algorithm, particularly during the selection and crossover phases. We also employ a global alignment technique with a binary mask to guide crossover operations more effectively. Experiments conducted on real Saccharomyces cerevisiae data confirm that integrating biological knowledge yields biologically meaningful and non-trivial biclusters, demonstrating the effectiveness of our approach. },
keywords = {Data analysis;Annotations;Evolutionary computation;Coherence;Biology;Gene expression;Object recognition;Bioinformatics;Artificial intelligence},
doi = {10.1109/ICTAI66417.2025.00101},
url = {https://doi.ieeecomputersociety.org/10.1109/ICTAI66417.2025.00101},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =Nov}