English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent developments in extracting and processing biological and clinical dataare allowing quantitative approaches to studying living systems.High-throughput sequencing, expression profiles, proteomics, and electronichealth records are some examples of such technologies. Extracting meaningfulinformation from those technologies requires careful analysis of the largevolumes of data they produce. In this note, we present a set of distributionsthat commonly appear in the analysis of such data. These distributions presentsome interesting features: they are discontinuous in the rational numbers, butcontinuous in the irrational numbers, and possess a certain self-similar(fractal-like) structure. The first set of examples which we present here aredrawn from a high-throughput sequencing experiment. Here, the self-similardistributions appear as part of the evaluation of the error rate of thesequencing technology and the identification of tumorogenic genomicalterations. The other examples are obtained from risk factor evaluation andanalysis of relative disease prevalence and co-mordbidity as these appear inelectronic clinical data. The distributions are also relevant to identificationof subclonal populations in tumors and the study of the evolution of infectiousdiseases, and more precisely the study of quasi-species and intrahost diversityof viral populations.

Fractal-like Distributions over the Rational Numbers in High-throughput Biological and Clinical Data