Can Zipf's law be adapted to normalize microarrays?

Background  

Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for varying organisms and tissues have shown that the majority of expressed genes exhibit a power-law distribution with an exponent close to -1 (i.e. obey Zipf's law). Based on the observation that our single channel and two channel microarray data sets also followed a power-law distribution, we were motivated to develop a normalization method based on this law, and examine how it compares with existing published techniques. A computationally simple and intuitively appealing technique based on this observation is presented.     

Intérêt complémentaire de la TEP/TDM au FDG et de l’imagerie conventionnelle dans le bilan initial et le suivi post-thérapeutique des cancers des VADS : recommandations et perspectives

Functional imaging by ¹⁸fluorodesoxyglucose (FDG) positron emission tomography (PET) and morphological imaging by computed tomography (CT) and magnetic resonance imaging (MRI) hold an important and complementary role in characterization of head and neck squamous cell carcinoma (HNSCC). Based on an exhaustive literature, the recommendations and perspectives of their use in the initial assessment and the post-therapeutic management of HNSCC are presented.