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

Many bioinformatics problems, such as sequence alignment, gene prediction,phylogenetic tree estimation and RNA secondary structure prediction, are oftenaffected by the "uncertainty" of a solution; that is, the probability of thesolution is extremely small. This situation arises for estimation problems onhigh-dimensional discrete spaces in which the number of possible discretesolutions is immense. In the analysis of biological data or the development ofprediction algorithms, this uncertainty should be handled carefully andappropriately. In this review, I will explain several methods to combat thisuncertainty, presenting a number of examples in bioinformatics. The methodsinclude (i) avoiding point estimation, (ii) maximum expected accuracy (MEA)estimations, and (iii) several strategies to design a pipeline involvingseveral prediction methods. I believe that the basic concepts and ideasdescribed in this review will be generally useful for estimation problems invarious areas of bioinformatics.

Fighting against uncertainty: An essential issue in bioinformatics