ArticlePloS one2011
Generalized centroid estimators in bioinformatics.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Rtools: A Web Server for Various Secondary Structural Analyses on Single RNA Sequences.Methods in molecular biology (Clifton, N.J.) · 2023Article
- HELIOS: High-speed sequence alignment in optics.PLoS computational biology · 2022Article
- Capturing alternative secondary structures of RNA by decomposition of base-pairing probabilities.BMC bioinformatics · 2018Article
- Rtools: a web server for various secondary structural analyses on single RNA sequences.Nucleic acids research · 2016Article
- Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs.BMC bioinformatics · 2015Article
- Direct updating of an RNA base-pairing probability matrix with marginal probability constraints.Journal of computational biology : a journal of computational molecular cell biology · 2012Article
- A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA).Journal of computational biology : a journal of computational molecular cell biology · 2012Review
- CentroidHomfold-LAST: accurate prediction of RNA secondary structure using automatically collected homologous sequences.Nucleic acids research · 2011Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In a number of estimation problems in bioinformatics, accuracy measures of the target problem are usually given, and it is important to design estimators that are suitable to those accuracy measures. However, there is often a discrepancy between an employed estimator and a given accuracy measure of the problem. In this study, we introduce a general class of efficient estimators for estimation problems on high-dimensional binary spaces, which represent many fundamental problems in bioinformatics. Theoretical analysis reveals that the proposed estimators generally fit with commonly-used accuracy measures (e.g. sensitivity, PPV, MCC and F-score) as well as it can be computed efficiently in many cases, and cover a wide range of problems in bioinformatics from the viewpoint of the principle of maximum expected accuracy (MEA). It is also shown that some important algorithms in bioinformatics can be interpreted in a unified manner. Not only the concept presented in this paper gives a useful framework to design MEA-based estimators but also it is highly extendable and sheds new light on many problems in bioinformatics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.