After scanning, the microarray data were normalized using quantile normalization in the R language environment (version 2.8.1, available at http://www.r-project.org/).org/
All calculations were performed using programming language R. For plate normalization and z-scoring R package cell HTS2 version 2.8.3 [ 30] was used.
Before normalization, the normal distribution and linear relations of Cy3 and Cy5 intensities were tested by qqplot and a linear regression model, respectively, in R statistical language.
The eMERGE consortium stimulated the creation of SHARPn (Strategic Health IT Advanced Research Projects) for normalization and natural language processing of EMR data [ 33] and CLIPMERGE (Clinical Implementation of Personalized Medicine Through Electronic Health Records and Genomics) for automated pharmacogenomics alerts [ 34].
Bio-LarK CR uses an Information Retrieval approach to index and retrieve HPO concepts, combined with a series of language techniques to enable term normalization and decomposition (e.g. token lexical variation).
They follow that link in raising the question what degree of normalization of receptive language scores would be needed to eradicate the risk of increased behaviour problems in children with permanent childhood hearing impairment (PCHI) compared with hearing children.
This raises the question of what degree of normalization of receptive language scores would be needed to eradicate the risk of increased behavioural problems in children with PCHI compared with hearing children.
Conventionally, features such as harmonics-to-noise ratio [27] and preprocessing methods like database normalization involving speaker, corpus, language, or gender are used to cope with the need of defining a reference criteria [14] in emotion detection.
The microarray data were extracted with Bead Studio 3.6 (Illumina, CA, USA) and normalized using the quantile normalization method in the Linear Models for Microarray Data (LIMMA) package in R language environment [15].
We achieve state-of-the-art accuracy on all languages in the TempEval-2 temporal normalization task, reporting a 4% improvement in both English and Spanish accuracy, and to our knowledge the first results for four other languages.
It is known that the normalization increases the speaker, corpus, language, or gender dependency while increasing the emotion recognition rate.
