Membrane computing inspired feature selection model for microarray cancer data

Cancer is the main public health issue in most places of the world due to the difficulties in its early diagnosis and to begin early treatment. In the recent years many techniques have been proposed to tackle the high dimensionality in microarray cancer datasets. This paper proposed a membrane-inspired feature selection method to utilize the potentials of membrane computing. In this regard, Kernel P system is modelled based on multi objective binary particle swarm optimization feature selection method through nine modeling steps briefly involving objects of compartments, labels of compartments, multiset of objects in the environment, system structure, and rules involved in each compartment. The proposed model evaluated by cell line data of colorectal cancer. As the initial attempt to come out with a model, division rule and sequential computation on Matlab are used as tools to define potential of membrane computing in trading space against time computation. The evaluation results through Weka indicate that selected marker genes through the proposed model generate high classification accuracy, precision, and recall metrics in compared with pure multi objective binary particle swarm optimization feature selection.