Evidence map›Paper›PMID 23029121›Full record

ArticlePloS one2012

Achieving high accuracy prediction of minimotifs.

Tian Mi, Sanguthevar Rajasekaran, Jerlin Camilus Merlin, Michael Gryk, Martin R Schiller

Abstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tian MiDepartment of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
Sanguthevar Rajasekaran
Jerlin Camilus Merlin
Michael Gryk
Martin R Schiller

Funding

Efficient Algorithms for Motif SearchR01LM010101 · NLM · UNIVERSITY OF CONNECTICUT STORRS · PI RAJASEKARAN, SANGUTHEVAR · 2010 to 2013
$1.4M
Building motif lexiconsR01GM079689 · NIGMS · UNIVERSITY OF NEVADA LAS VEGAS · PI SCHILLER, MARTIN R · 2007 to 2010
$1.1M
NIGMS NIH HHS GM079689NIGMS NIH HHS R01 GM079689NLM NIH HHS LM010101NLM NIH HHS R01 LM010101
6 · The paper itself

Abstract

The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network models. The minimotif multi-filter with its excellent accuracy represents the state-of-the-art in minimotif prediction and is expected to be very useful to biologists investigating protein function and how missense mutations cause disease.

Indexed as

Amino Acid MotifsAlgorithmsComputational BiologyInternetModels, TheoreticalPattern Recognition, AutomatedProtein BindingProteinsROC CurveProteins

Identifiers

PMID23029121
PMCPMC3459956

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.