An ensemble strategy for Haplotype Inference based on the internal variability of algorithms

摘要

In this paper, we present an ensemble strategy for haplotype inference problem. The proposed approach generates an ensemble solution from several haplotype matrices yielded by a non-deterministic algorithm. We performed extensive experiments and statistical performance evaluation. Besides the inference accuracy based on Switch Error, our analysis controls the execution time as well. The results show that the proposed method: (1) generates more accurate solutions compared to the existing strategies, (2) improves the precision of haplotyping techniques, such as fastPHASE, Beagle, and Mach, and (3) the Beagle based ensemble produced solutions with quality comparable to the more accurate but more computing intensive method: fastPHASE.