2022

Conférence

GECCO Companion 2022: 344-347

The behavior of many diseases is still not well understood by researchers. Genome-Wide Association (GWA) analyzes have recently become a popular approach to discovering the genetic causes of many complex diseases. These analyzes could lead to the discovery of genetic factors potentially involved in certain disease susceptibility. These studies typically use the most common genetic variation between individuals, the Single Nucleotide Polymorphism (SNP). Indeed, many complex diseases have been revealed to be associated with combinations of SNP interactions. However, the identification of such interactions is considered difficult. Therefore, various unsupervised data mining methods are often developed in the literature to identify such variation involved in disease. In this work, a biclustering method is adopted to detect possible associations between SNP markers and disease susceptibility. It is an unsupervised classification technique, which plays an increasingly important role in the study of modern biology. We propose an evolutionary algorithm based on a bi-objective approach for the biclustering of the Genome-Wide Association. An experimental study is achieved on synthetic data to evaluate the performance of the proposed algorithm. Promising results are obtained.

@inproceedings{DBLP:conf/gecco/MaatoukABA22,
  author       = {Ons Ma{\^{a}}touk and
                  Emna Ayari and
                  Hend Bouziri and
                  Wassim Ayadi},
  editor       = {Jonathan E. Fieldsend and
                  Markus Wagner},
  title        = {{BOBEA:} a bi-objective biclustering evolutionary algorithm for genome-wide
                  association analysis},
  booktitle    = {{GECCO} '22: Genetic and Evolutionary Computation Conference, Companion
                  Volume, Boston, Massachusetts, USA, July 9 - 13, 2022},
  pages        = {344--347},
  publisher    = {{ACM}},
  year         = {2022},
  url          = {https://doi.org/10.1145/3520304.3528802},
  doi          = {10.1145/3520304.3528802},
  timestamp    = {Mon, 05 Feb 2024 20:29:03 +0100},
  biburl       = {https://dblp.org/rec/conf/gecco/MaatoukABA22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}