Gene Clustering Using Metaheuristic Optimization Algorithms

Abstract

Gene clustering is a familiar step in the exploratory analysis of high dimensional biological data. It is the process of grouping genes of similar patterns in the same cluster and aims at analyzing the functions of gene that leads to the development of drugs and early diagnosis of diseases. In the recent years, much research has been proposed using nature inspired meta-heuristic algorithms. Cuckoo Search is one such optimization algorithm inspired from nature by breeding strategy of parasitic bird, the cuckoo. This paper proposes cuckoo search clustering and clustering using levy flight cuckoo search for grouping brain tumor gene expression dataset. A comparative study is made with genetic algorithm, PSO clustering, cuckoo search clustering and clustering using levy flight cuckoo search. Levy flight is an important property of levy distribution which covers the entire search space. Breeding pattern of cuckoo is associated with the genes that cause tumor to grow and affect other organs gradually. Clusters generated by these algorithms are validated to find the closeness among the genes in a cluster and separation of genes between clusters. Experimental results carried out in this paper show that cuckoo search clustering outperforms other clustering methods used for experimentation.

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

“…In [138], the authors also use a PSO metaheuristic to automatically cluster data from students in a learning management system (LMS) to adapt teaching resources to specific students' needs. Gene clustering is performed in [139], where a comparative study is presented based on the following metaheuristics: GA, PSO, cuckoo search and levy flight cuckoo search. More recently, [140] present a GRASP metaheuristic for biclustering (i.e., considering both genes and conditions) of gene expression data.…”

Hybrid Wrapper/Filter Gene Selection Using an Ensemble of Classifiers and PSO Algorithm

“…As a general observation, there are several problems studied in genes expression microarrays (GEM). All of them can be divided into three classes namely the class prediction which uses supervised machine learning approaches, the class discovery which uses unsupervised machine learning approaches (Banu & Andrews, 2015) and the class gene comparison that uses machine learning approaches in general (Golub et al, 1999). The direct application of these methods on highdimensional data is usually ineffective (Wu et al, 2012).…”