Matrix completion with weighted constraint for haplotype estimation

Estimation of haplotype sequences from DNA sequencing samples is a challenging task whose mathematical formulation leads to an NP-hard problem. Also, accuracy the estimates plays essential role in providing required information for personalized medicine. In order fully incorporate available quality measurements with higher into estimates, this paper, we propose new optimization design using weighted version well-established matrix completion approach. This performed by penalizing difference between and desired some weights, which are used form constraint. Accordingly, derive corresponding error bound matrix, shows that larger noise power increases estimation factor proportional inverse mentioned weights. devising algorithm called Haplotype reconstruction nuclear norm minimization Weighted Constraint (HapWeC). Computer simulations show outperformance HapWeC compared recent algorithms terms normalized rate.