CNV_IWOABP: Collaboration of Improved Whale Optimization Algorithm and BP Neural Networks for Copy Number Variations

Copy number variation (CNV) is a remarkable manifestation of genomic structural variations that affect human health. However, CNV detection in low coverage and low purity data is one of the challenging issues. To fill this gap, a hybrid algorithm combines an improved whale optimization algorithm (IWOA) and backpropagation (BP) neural networks (hereafter called IWOABP) is developed for CNV detection. First, to enhance the precision of detection, the detectable categories for the gain and loss are respectively expanded to two types, where gain is divided into tand_gain and inte_gain, and loss is divided into hemi_loss and homojoss. Then, IWOA is introduced to tune the weights and bias values of BP neural network, which can improve the BP neural network abilities to jump out of the local optimums. Next, to ensure the population diversity and the uniform distribution of solutions, a pooling mechanism and a migration search strategy are designed. In addition, to balance the exploitation and exploration abilities, three position update strategies based on an adaptive inertia-weight are used. Finally, to evaluate the detection performance of IWOABP, seven state-of-the-art detection methods are chosen to make detailed comparisons with the proposed algorithm. The results show that IWOABP has outstanding performance in sensitivity, precision, and Fl-score using both simulated and real data.