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Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks

Overview of attention for article published in BMC Systems Biology, March 2015
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4 tweeters

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2 Dimensions

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14 Mendeley
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Title
Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks
Published in
BMC Systems Biology, March 2015
DOI 10.1186/s12918-015-0154-2
Pubmed ID
Authors

Takanori Hasegawa, Tomoya Mori, Rui Yamaguchi, Teppei Shimamura, Satoru Miyano, Seiya Imoto, Tatsuya Akutsu

Abstract

As a result of recent advances in biotechnology, many findings related to intracellular systems have been published, e.g., transcription factor (TF) information. Although we can reproduce biological systems by incorporating such findings and describing their dynamics as mathematical equations, simulation results can be inconsistent with data from biological observations if there are inaccurate or unknown parts in the constructed system. For the completion of such systems, relationships among genes have been inferred through several computational approaches, which typically apply several abstractions, e.g., linearization, to handle the heavy computational cost in evaluating biological systems. However, since these approximations can generate false regulations, computational methods that can infer regulatory relationships based on less abstract models incorporating existing knowledge have been strongly required. We propose a new data assimilation algorithm that utilizes a simple nonlinear regulatory model and a state space representation to infer gene regulatory networks (GRNs) using time-course observation data. For the estimation of the hidden state variables and the parameter values, we developed a novel method termed a higher moment ensemble particle filter (HMEnPF) that can retain first four moments of the conditional distributions through filtering steps. Starting from the original model, e.g., derived from the literature, the proposed algorithm can sequentially evaluate candidate models, which are generated by partially changing the current best model, to find the model that can best predict the data. For the performance evaluation, we generated six synthetic data based on two real biological networks and evaluated effectiveness of the proposed algorithm by improving the networks inferred by previous methods. We then applied time-course observation data of rat skeletal muscle stimulated with corticosteroid. Since a corticosteroid pharmacogenomic pathway, its kinetic/dynamics and TF candidate genes have been partially elucidated, we incorporated these findings and inferred an extended pathway of rat pharmacogenomics. Through the simulation study, the proposed algorithm outperformed previous methods and successfully improved the regulatory structure inferred by the previous methods. Furthermore, the proposed algorithm could extend a corticosteroid related pathway, which has been partially elucidated, with incorporating several information sources.

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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 36%
Student > Ph. D. Student 3 21%
Student > Doctoral Student 2 14%
Other 1 7%
Student > Bachelor 1 7%
Other 2 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 36%
Biochemistry, Genetics and Molecular Biology 3 21%
Computer Science 2 14%
Mathematics 1 7%
Immunology and Microbiology 1 7%
Other 2 14%

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 19 April 2015.
All research outputs
#11,730,342
of 19,211,930 outputs
Outputs from BMC Systems Biology
#527
of 1,129 outputs
Outputs of similar age
#137,689
of 273,369 outputs
Outputs of similar age from BMC Systems Biology
#1
of 1 outputs
Altmetric has tracked 19,211,930 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,129 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 48th percentile – i.e., 48% 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 273,369 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them