Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization

The computational discovery of DNA motifs is one of the most important problems in molecular biology and computational biology, and it has not yet been resolved in an efficient manner. With previous research, we have solved the single-objective motif discovery problem (MDP) based on biogeography-based optimization (BBO) and gained excellent results. In this study, we apply multi-objective biogeography-based optimization algorithm to the multi-objective motif discovery problem, which refers to discovery of novel transcription factor binding sites in DNA sequences. For this, we propose an improved multi-objective hybridization of adaptive Biogeography-Based Optimization with differential evolution (DE) approach, namely MHABBO, to predict motifs from DNA sequences. In the MHABBO algorithm, the fitness function based on distribution information among the habitat individuals and the Pareto dominance relation are redefined. Based on the relationship between the cost of fitness function and average cost in each generation, the MHABBO algorithm adaptively changes the migration probability and mutation probability. Additionally, the mutation procedure that combines with the DE algorithm is modified. And the migration operators based on the number of iterations are improved to meet motif discovery requirements. Furthermore, the immigration and emigration rates based on a cosine curve are modified. It can therefore generate promising candidate solutions. Statistical comparisons with DEPT and MOGAMOD approaches on three commonly used datasets are provided, which demonstrate the validity and effectiveness of the MHABBO algorithm. Compared with some typical existing approaches, the MHABBO algorithm performs better in terms of the quality of the final solutions.