EasyGene--a prokaryotic gene finder that ranks ORFs by statistical significance (Q24797596)

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English	EasyGene--a prokaryotic gene finder that ranks ORFs by statistical significance	scientific article

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instance of

scholarly article

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title

EasyGene--a prokaryotic gene finder that ranks ORFs by statistical significance (English)

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main subject

statistics

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author

Anders Krogh

series ordinal

2

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author name string

Thomas Schou Larsen

series ordinal

1

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language of work or name

English

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publication date

3 June 2003

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published in

BMC Bioinformatics

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volume

4

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issue

1

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page(s)

21

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cites work

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GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions

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Selection of representative protein data sets

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A probabilistic method for identifying start codons in bacterial genomes.

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On the total number of genes and their length distribution in complete microbial genomes

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A novel bacterial gene-finding system with improved accuracy in locating start codons

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Finding prokaryotic genes by the 'frame-by-frame' algorithm: targeting gene starts and overlapping genes.

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Optimally parsing a sequence into different classes based on multiple types of evidence

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Identifiers

DOI

10.1186/1471-2105-4-21

1 reference

stated in

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

4325859

DBLP publication ID

journals/bmcbi/LarsenK03

1 reference

stated in

DBLP Dataset 2021-01-02

retrieved

28 January 2021

Dimensions Publication ID

1013695495

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OpenCitations bibliographic resource ID

4325859

1 reference

stated in

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

4325859

PMC publication ID

521197

1 reference

stated in

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

4325859

PubMed publication ID

12783628

1 reference

stated in

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

4325859

ResearchGate publication ID

10727622

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