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Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer

Overview of attention for article published in BMC Bioinformatics, February 2016
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Title
Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0951-7
Pubmed ID
Authors

Meng-Yun Wu, Xiao-Fei Zhang, Dao-Qing Dai, Le Ou-Yang, Yuan Zhu, Hong Yan

Abstract

To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility. In this article, we propose a regularized logistic regression model. Different from previous methods which focus on individual genes or modules, our model takes gene pairs, which are connected in a protein-protein interaction network, into account. A line graph is constructed to represent the adjacencies between pairwise interactions. Based on this line graph, we incorporate the degree information in the model via an adaptive elastic net, which makes our model less dependent on the expression data. Experimental results on six publicly available breast cancer datasets show that our method can not only achieve competitive performance in classification, but also retain great stability in variable selection. Therefore, our model is able to identify the diagnostic and prognostic biomarkers in a more robust way. Moreover, most of the biomarkers discovered by our model have been verified in biochemical or biomedical researches. The proposed method shows promise in the diagnosis of disease pathogenesis with different clinical characteristics. These advances lead to more accurate and stable biomarker discovery, which can monitor the functional changes that are perturbed by diseases. Based on these predictions, researchers may be able to provide suggestions for new therapeutic approaches.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 1%
Unknown 74 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 20%
Researcher 8 11%
Student > Bachelor 5 7%
Student > Master 5 7%
Student > Postgraduate 4 5%
Other 8 11%
Unknown 30 40%
Readers by discipline Count As %
Computer Science 16 21%
Agricultural and Biological Sciences 9 12%
Biochemistry, Genetics and Molecular Biology 6 8%
Medicine and Dentistry 4 5%
Mathematics 3 4%
Other 6 8%
Unknown 31 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 06 March 2016.
All research outputs
#13,225,592
of 22,852,911 outputs
Outputs from BMC Bioinformatics
#4,009
of 7,292 outputs
Outputs of similar age
#138,541
of 297,542 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 133 outputs
Altmetric has tracked 22,852,911 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,292 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 297,542 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.