In data fusion, score normalization is a step to make scores, which are obtained from different component systems for all documents, comparable to each other. It is an indispensable step for effective data fusion algorithms such as CombSum and CombMNZ to combine them. In this paper, we evaluate four linear score normalization methods, namely the fitting method, Zero-one, Sum, and ZMUV, through ...

MOTIVATION The focus of this paper is on two new normalization methods for cDNA microarrays. After the image analysis has been performed on a microarray and before differentially expressed genes can be detected, some form of normalization must be applied to the microarrays. Normalization removes biases towards one or other of the fluorescent dyes used to label each mRNA sample allowing for prop...

Tokenization in the bioscience domain is often difficult. New terms, technical terminology, and nonstandard orthography, all common in bioscience text, contribute to this difficulty. This paper will introduce the tasks of tokenization, normalization before introducing BAccHANT, a system built for bioscience text normalization. Casting tokenization / normalization as a problem of punctuation cla...

When dealing with large scale gene expression studies, observations are commonly contaminated by sources of unwanted variation such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g. when the goal is to cluster the samples or to build a correc...

This paper addresses the issue of text normalization on non-standard Italian data. We present TweetNorm1, a system which normalizes Italian tweets in a way that the amount of microblog slang and distorted text appearance is drastically reduced and the normalized output has a much cleaner and more formal style. The paper shows that with a set of fixed language-independent rules and trained rules...

Normalization is a prerequisite for almost all follow-up steps in microarray data analysis. Accurate normalization across different experiments and phenotypes assures a common base for comparative yet quantitative studies using gene expression data. In this paper, we report a comparison study of four normalization approaches, namely, linear regression (LR), Loess regression, invariant ranking (...

The Pharmacogenomics Knowledge Base (PharmGKB) [1] is a publicly available central resource for pharmacogenomics data and knowledge, which is being widely employed into clinical practice and thus requires using clinical terminologies. Hence, harmonizing PharmGKB drug data with well annotated drug terminologies will facilitate its integration with other related resources and support data represe...

A method commonly used to “time-normalize” gait data (here referred to as linear length normalization [LLN]) is to linearly convert the trajectory’s time axis from the experimentally-recorded time units to an axis representing percentage of the gait cycle. However, other time-normalization techniques are also possible, such as dynamic time warping [DTW] and derivative dynamic time warping [DDTW...

Hyperclique patterns are groups of objects which are strongly related to each other. Indeed, the objects in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by uncentered Pearson’s correlation coefficient. Recent literature has provided the approach to discovering hyperclique patterns over data sets with binary attributes. In this paper, we ...