Erratum to: Feature selection environment for genomic applications
نویسندگان
چکیده
منابع مشابه
GENOMIC SELECTION Genomic Selection in Multi-environment Crop Trials
Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed ...
متن کاملA New Framework for Distributed Multivariate Feature Selection
Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...
متن کاملEvaluation Metrics for Feature Selection in Population Genomic Data
Single Nucleotide Polymorphisms (SNPs) are considered nowadays one of the most important class of genetic markers with a wide range of applications with both scientific and economic interests. Although the advance of biotechnology has made feasible the production of genome wide SNP datasets, the cost of the production is still high. The transformation of the initial dataset into a smaller one w...
متن کاملFeature Selection for Genomic and Proteomic Data Mining
The extreme dimensionality (also known as the curse of dimensionality) in genomic data has been traditionally a serious concern inmany applications. This hasmotivated a lot of research in feature representation and selection, both aiming at reducing dimensionality of features to facilitate training and prediction of genomic data. In this chapter,N denotes the number of training data samples,M t...
متن کاملFeature selection for high-dimensional genomic microarray data
We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our approach is a hybrid of filter and wrapper approaches to feature selection. We make use of a sequence of simple filters, culminating in Koller and Sahami’s (1996) Markov Blanket filter, to decide on particular featur...
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: BMC Bioinformatics
سال: 2009
ISSN: 1471-2105
DOI: 10.1186/1471-2105-10-285