TargetBoost statistics

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Citations per year

Number of citations per year for the bioinformatics software tool TargetBoost

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This map represents all the scientific publications referring to TargetBoost per scientific context
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TargetBoost specifications


Unique identifier OMICS_27544
Name TargetBoost
Interface Web user interface
Restrictions to use None
Computer skills Basic
Maintained No


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Publication for TargetBoost

TargetBoost citations


The miR 125 family is an important regulator of the expression and maintenance of maternal effect genes during preimplantational embryo development

Open Biol
PMCID: 5133438
PMID: 27906131
DOI: 10.1098/rsob.160181

[…] ne the regulatory mechanisms between miRNAs and proteins. Numerous computational approaches for miRNA target prediction have already been developed, such as TargetScan, miRanda, miRmap, Diana-MicroT, TargetBoost, miTarget, MirTarget2, TargetSpy, TargetMiner, MultiMiTar, NBmiRTar and microT-ANN []. Because each algorithm has its own set of limitations, multiple computational algorithms are commonly […]


Advances in the Techniques for the Prediction of microRNA Targets

Int J Mol Sci
PMCID: 3645737
PMID: 23591837
DOI: 10.3390/ijms14048179

[…] achine learning algorithms can also be used to intelligently search for the parameters with most predictive power of genuine miRNA binding sites. An example of a method for miRNA target prediction is TargetBoost, which uses machine learning based on a set of validated miRNA targets in lower organisms to create weighted sequence motifs that capture binding characteristics between miRNAs and their t […]


One Decade of Development and Evolution of MicroRNA Target Prediction Algorithms

PMCID: 5054202
PMID: 23200135
DOI: 10.1016/j.gpb.2012.10.001

[…] ly. Representatives from this line are briefly described as follows. Since machine learning algorithms strongly rely on experimental data, we also specify the size of the respective training dataset.•TargetBoost consists of a boosting algorithm that assigns weights to sequence patterns of 30 nucleotides. The negative dataset used for training consists of 300 randomly-generated sequences, and the […]

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TargetBoost institution(s)
Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway; Interagon AS, Medisinsk teknisk senter, Trondheim, Norway
TargetBoost funding source(s)
Supported by the Norwegian Research Council, grant 151899/150, and the bioinformatics platform at the Norwegian University of Science and Technology, Trondheim, Norway.

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