A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

Abstract

Microarray technology is beneficial in terms of diagnosing various diseases, including cancer. Despite all DNA microarray benefits, the high number of genes versus the low number of samples has always been a crucial challenge for this technology. Accordingly, we need new optimization algorithms to select optimal genes for faster disease diagnosis. In this article, a new version of the binary cat optimization algorithm, named SBCSO, for gene selection in DNA microarray expression cancer data is presented. The main contributions in this paper are listed as follows: First, the opposition-based learning (OBL) mechanism is employed to improve the proposed algorithm's population members' diversity. Second, a time-varying V-shape transfer function is employed to balance the two phases of exploration and extraction in the proposed algorithm. Third, the MR and 𝛌 parameters in the proposed algorithm are adapted over time, and finally, single-objective and multi-objective approaches are proposed to solve the gene selection problems. The fifteen datasets pertinent to microarray data of various cancer types are employed to compare the proposed method with other well-known binary optimization algorithms. The experiments' results indicate that the proposed algorithm has a better capability to select the optimal genes for a faster disease diagnosis.

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

“…Single-objective and multi-objective solutions address the gene selection challenges. Evaluation across 15 microarray datasets covering various cancer types demonstrates the algorithm's superior capability in selecting optimal genes for expedited disease diagnosis [23]. BioSurv, is a framework designed for the identification of cancer biomarkers and predicts cancer survival outcomes.…”