Abstract:
:Many bioinformatics problems, such as sequence alignment, gene prediction, phylogenetic tree estimation and RNA secondary structure prediction, are often affected by the 'uncertainty' of a solution, that is, the probability of the solution is extremely small. This situation arises for estimation problems on high-dimensional discrete spaces in which the number of possible discrete solutions is immense. In the analysis of biological data or the development of prediction algorithms, this uncertainty should be handled carefully and appropriately. In this review, I will explain several methods to combat this uncertainty, presenting a number of examples in bioinformatics. The methods include (i) avoiding point estimation, (ii) maximum expected accuracy (MEA) estimations and (iii) several strategies to design a pipeline involving several prediction methods. I believe that the basic concepts and ideas described in this review will be generally useful for estimation problems in various areas of bioinformatics.
journal_name
Brief Bioinformjournal_title
Briefings in bioinformaticsauthors
Hamada Mdoi
10.1093/bib/bbt038subject
Has Abstractpub_date
2014-09-01 00:00:00pages
748-67issue
5eissn
1467-5463issn
1477-4054pii
bbt038journal_volume
15pub_type
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