Imputing Missing Genotypes with WeightedkNearest Neighbors
نویسندگان
چکیده
منابع مشابه
Imputing missing genotypes with weighted k nearest neighbors.
Missing values are a common problem in genetic association studies concerned with single-nucleotide polymorphisms (SNPs). Since many statistical methods cannot handle missing values, such values need to be removed prior to the actual analysis. Considering only complete observations, however, often leads to an immense loss of information. Therefore, procedures are required that can be used to im...
متن کاملImputing missing genotypes: effects of methods and patterns of missing data
Costs of high-throughput genotyping have decreased to the point where it appears economically feasible to use molecular genetic marker information in applied breeding programs. Some practical questions remain to be addressed about how best to deal with missing data in the resulting genotype datasets, to minimize the impact of the missing data on the accuracy of breeding value prediction. Data c...
متن کاملCost-Sensitive Imputing Missing Values with Ordering
Missing value is an unavoidable problem when dealing with real world data sources, and various approaches for dealing with missing data have been developed. In fact, it is very important to consider the imputation ordering (ordering means which missing value should be imputed at first with the help of a specific criterion) during the imputation process, because not all attributes have the same ...
متن کاملImputing responses that are not missing
We consider estimation of linear functionals of the joint law of regression models in which responses are missing at random. The usual approach is to work with the fully observed data, and to replace unobserved quantities by estimators of appropriate conditional expectations. Another approach is to replace all quantities by such estimators. We show that the second method is usually better than ...
متن کاملRecover Missing Sensor Data with Iterative Imputing Network
Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly. However, due to systematic or accidental mis-operations, sensor data comes very often with a variety of missing values, resulting in considerable difficulties in the follow-up analysis and visualization. Previous work imputes the missing values by in...
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: Journal of Toxicology and Environmental Health, Part A
سال: 2012
ISSN: 1528-7394,1087-2620
DOI: 10.1080/15287394.2012.674910