English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Single Nucleotide Polymorphisms (SNPs), a single DNA base varying from one individual to another, are believed to be the most frequent form responsible for genetic differences. Haplotypes have more information for disease-associating than individual SNPs or genotypes; it is substantially more difficult to determine haplotypes through experiments. Hence, computational methods that can reduce the cost of determining haplotypes become attractive alternatives. MEC, as a standard model for haplotype reconstruction, is fed by fragments input to infer the best pair of haplotypes with minimum errors needing correction. It is proved that haplotype reconstruction in the MEC model is a NP-Hard problem. Thus, researchers’ desire reduced running time and obtaining acceptable results. Heuristic algorithms and different clustering methods are employed to achieve these goals. In this paper, Harmony Search (HS) is considered a clustering approach. Extensive computational experiments indicate that the designed HS algorithm achieves a higher accuracy than the genetic algorithm (GA) or particle swarm optimization (PSO) to the MEC model in most cases.

Using Harmony Clustering for Haplotype Reconstruction from SNP fragments