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EMQIT: a machine learning approach for energy based PWM matrix quality improvement

Overview of attention for article published in Biology Direct, August 2017
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EMQIT: a machine learning approach for energy based PWM matrix quality improvement
Published in
Biology Direct, August 2017
DOI 10.1186/s13062-017-0189-y
Pubmed ID

Karolina Smolinska, Marcin Pacholczyk


Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high-throughput methods such as, e.g., ChiP-seq the need to provide unbiased models of binding events has been made apparent. We present EMQIT a modification to the approach introduced by Alamanova et al. and later implemented as 3DTF server. We observed that tuning of Boltzmann factor weights, used for conversion of calculated energies to nucleotide probabilities, has a significant impact on the quality of the associated PWM matrix. Consequently, we proposed to use receiver operator characteristics curves and the 10-fold cross-validation to learn best weights using experimentally verified data from TRANSFAC database. We applied our method to data available for various TFs. We verified the efficiency of detecting TF binding sites by the 3DTF matrices improved with our technique using experimental data from the TRANSFAC database. The comparison showed a significant similarity and comparable performance between the improved and the experimental matrices (TRANSFAC). Improved 3DTF matrices achieved significantly higher AUC values than the original 3DTF matrices (at least by 0.1) and, at the same time, detected notably more experimentally verified TFBSs. The resulting new improved PWM matrices for analyzed factors show similarity to TRANSFAC matrices. Matrices had comparable predictive capabilities. Moreover, improved PWMs achieve better results than matrices downloaded from 3DTF server. Presented approach is general and applicable to any energy-based matrices. EMQIT is available online at http://biosolvers.polsl.pl:3838/emqit . This article was reviewed by Oliviero Carugo, Marek Kimmel and István Simon.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 22%
Student > Bachelor 4 22%
Student > Master 2 11%
Professor > Associate Professor 2 11%
Professor 1 6%
Other 3 17%
Unknown 2 11%
Readers by discipline Count As %
Computer Science 6 33%
Engineering 4 22%
Medicine and Dentistry 3 17%
Agricultural and Biological Sciences 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 0 0%
Unknown 3 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 August 2017.
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Outputs from Biology Direct
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Outputs of similar age
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Outputs of similar age from Biology Direct
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Altmetric has tracked 16,639,069 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 597 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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