Influential Gene Selection From High-Dimensional Genomic Data Using a Bio-Inspired Algorithm Wrapped Broad Learning System

Influential Gene Selection From High-Dimensional Genomic Data Using a Bio-Inspired Algorithm Wrapped Broad Learning System

Blog Article

The classification of high dimensional gene expression/ microarray data always plays an important role in various disease Skateboards diagnoses and drug discovery.To avoid the curse of high dimensionality, the selection of the most influential genes remains a challenging task for the researchers in the machine learning field.As the extraction of influential features by a bio-inspired algorithm is a non-deterministic polynomial-time (NP-Hard) task, the possibility of applying new algorithm is always there.In this suggested work, a recently developed bio-inspired algorithm, Monarch Butterfly Optimization (MBO), is wrapped with the Broad Learning System (BLS), called MBO-BLS, to choose the most influential features and classify the microarray data simultaneously.

In the first stage, a pre-selection method (Relief) is used to select a feature subset.Then, this selected feature subset undergoes further execution with the MBO-BLS model.To estimate the efficacy of the presented model, six cancerous microarray datasets are taken.Here, sensitivity, specificity, precision, F-score, Kappa, and MCC measures are used for an impartial comparison.

Further, to prove the supremacy of the presented method, the basic BLS, Genetic Algorithm wrapped BLS (GA-BLS), Particle Swarm Optimization wrapped BLS (PSO-BLS), and the existing ten models are taken for comparison.Moreover, to examine the designed model statistically, Analysis of variance (ANOVA) test is also performed here.From the above qualitative and quantitative QUICK-BLAST analysis, it is concluded that the proposed MBO-BLS model outclasses other considering models.