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Comparative network stratification analysis for identifying functional interpretable network biomarkers

Overview of attention for article published in BMC Bioinformatics, March 2017
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Title
Comparative network stratification analysis for identifying functional interpretable network biomarkers
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1462-x
Pubmed ID
Authors

Chuanchao Zhang, Juan Liu, Qianqian Shi, Tao Zeng, Luonan Chen

Abstract

A major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. In this paper, we present a new computational framework based on an integer programming model, named as Comparative Network Stratification (CNS), to extract functional or interpretable network biomarkers, which are of strongly discriminative power on disease states and also readable interpretation on biological functions. In addition, CNS can not only recognize the pathogen biological functions disregarded by traditional Expression-based/Network-based methods, but also uncover the active network-structures underlying such dysregulated functions underestimated by traditional Function-based methods. To validate the effectiveness, we have compared CNS with five state-of-the-art methods, i.e. GSVA, Pathifier, stSVM, frSVM and AEP on four datasets of different complex diseases. The results show that CNS can enhance the discriminative power of network biomarkers, and further provide biologically interpretable information or disease pathogenic mechanism of these biomarkers. A case study on type 1 diabetes (T1D) demonstrates that CNS can identify many dysfunctional genes and networks previously disregarded by conventional approaches. Therefore, CNS is actually a powerful bioinformatics tool, which can identify functional or interpretable network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. CNS was implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/CNSpackage_0.1.rar .

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 23%
Student > Ph. D. Student 9 23%
Student > Bachelor 3 8%
Student > Master 3 8%
Unspecified 3 8%
Other 4 10%
Unknown 8 21%
Readers by discipline Count As %
Computer Science 8 21%
Agricultural and Biological Sciences 6 15%
Medicine and Dentistry 5 13%
Unspecified 3 8%
Biochemistry, Genetics and Molecular Biology 3 8%
Other 2 5%
Unknown 12 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 March 2018.
All research outputs
#10,521,893
of 17,897,867 outputs
Outputs from BMC Bioinformatics
#3,743
of 6,314 outputs
Outputs of similar age
#138,439
of 274,829 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 23 outputs
Altmetric has tracked 17,897,867 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,314 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 37th percentile – i.e., 37% 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 274,829 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.