Haplotyping with missing data via perfect path phylogenies
نویسندگان
چکیده
منابع مشابه
Haplotyping with missing data via perfect path phylogenies
Computational methods for inferring haplotype information from genotype data are used in studying the association between genomic variation and medical condition. Recently, Gusfield proposed a haplotype inference method that is based on perfect phylogeny principles. A fundamental problem arises when one tries to apply this approach in the presence of missing genotype data, which is common in pr...
متن کاملPerfect Path Phylogeny Haplotyping with Missing Data Is Fixed-Parameter Tractable
Haplotyping via perfect phylogeny is a method for retrieving haplotypes from genotypes. Fast algorithms are known for computing perfect phylogenies from complete and error-free input instances—these instances can be organized as a genotype matrix whose rows are the genotypes and whose columns are the single nucleotide polymorphisms under consideration. Unfortunately, in the more realistic setti...
متن کاملHaplotyping as Perfect Phylogeny
For diploid organisms (e.g. humans), each chromosome is present in two non-exact copies and the description of all the data from a single chromosome is called a haplotype. Obtaining haplotype data is important in applications such as analyzing complex diseases, however this is a very difficult problem to solve experimentally and finding mixed genotype data is much less technically difficult and...
متن کاملHaplotyping as Perfect Phylogeny: A Direct Approach
A full haplotype map of the human genome will prove extremely valuable as it will be used in large-scale screens of populations to associate specific haplotypes with specific complex genetic-influenced diseases. A haplotype map project has been announced by NIH. The biological key to that project is the surprising fact that some human genomic DNA can be partitioned into long blocks where geneti...
متن کاملDEA with Missing Data: An Interval Data Assignment Approach
In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the prop...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Discrete Applied Mathematics
سال: 2007
ISSN: 0166-218X
DOI: 10.1016/j.dam.2005.09.020