Z
Zhenping Li
Researcher at Beijing Wuzi University
Publications - 15
Citations - 374
Zhenping Li is an academic researcher from Beijing Wuzi University. The author has contributed to research in topics: Complex network & Bipartite graph. The author has an hindex of 10, co-authored 15 publications receiving 364 citations.
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Journal ArticleDOI
Haplotype reconstruction from SNP fragments by minimum error correction
TL;DR: To improve the MEC model for haplotype reconstruction, a new computational model is proposed, which simultaneously employs genotype information of an individual in the process of SNP correction, and is called MEC with genotypes information (shortly, MEC/GI).
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A parsimonious tree-grow method for haplotype inference
TL;DR: A novel algorithm for the haplotype inference problem with the parsimony criterion is developed, based on a parsimonious tree-grow method (PTG), a heuristic algorithm that can find the minimum number of distinct haplotypes based on the criterion of keeping all genotypes resolved during tree- grow process.
Posted Content
Quantitative Function and Algorithm for Community Detection in Bipartite Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a new quantitative function for community detection in bipartite networks, and demonstrate that this quantitative function is superior to the widely used Barber's bipartitite modularity and other functions.
Journal ArticleDOI
Quantitative function and algorithm for community detection in bipartite networks
TL;DR: A new quantitative function for community detection in bipartite networks is proposed and it is demonstrated that this quantitative function is superior to the widely used Barber's bipartites modularity and other functions and applies to both artificial networks and real-world networks.
Journal ArticleDOI
Detecting drug targets with minimum side effects in metabolic networks
TL;DR: A novel approach was proposed to exactly formulate this drug target detection problem as an integer linear programming model, which ensures that optimal solutions can be found efficiently without any heuristic manipulations and can be applied to large-scale networks including the whole metabolic networks from most organisms.