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Towards targeted combinatorial therapy design for the treatment of castration-resistant prostate cancer

Overview of attention for article published in BMC Bioinformatics, March 2017
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1 tweeter

Citations

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19 Dimensions

Readers on

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16 Mendeley
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Title
Towards targeted combinatorial therapy design for the treatment of castration-resistant prostate cancer
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1522-2
Pubmed ID
Authors

Osama Ali Arshad, Aniruddha Datta

Abstract

Prostate cancer is one of the most prevalent cancers in males in the United States and amongst the leading causes of cancer related deaths. A particularly virulent form of this disease is castration-resistant prostate cancer (CRPC), where patients no longer respond to medical or surgical castration. CRPC is a complex, multifaceted and heterogeneous malady with limited standard treatment options. The growth and progression of prostate cancer is a complicated process that involves multiple pathways. The signaling network comprising the integral constituents of the signature pathways involved in the development and progression of prostate cancer is modeled as a combinatorial circuit. The failures in the gene regulatory network that lead to cancer are abstracted as faults in the equivalent circuit and the Boolean circuit model is then used to design therapies tailored to counteract the effect of each molecular abnormality and to propose potentially efficacious combinatorial therapy regimens. Furthermore, stochastic computational modeling is utilized to identify potentially vulnerable components in the network that may serve as viable candidates for drug development. The results presented herein can aid in the design of scientifically well-grounded targeted therapies that can be employed for the treatment of prostate cancer patients.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 19%
Student > Ph. D. Student 3 19%
Student > Doctoral Student 2 13%
Student > Bachelor 1 6%
Other 1 6%
Other 3 19%
Unknown 3 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 25%
Engineering 3 19%
Medicine and Dentistry 2 13%
Neuroscience 2 13%
Computer Science 1 6%
Other 0 0%
Unknown 4 25%

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 01 April 2017.
All research outputs
#8,038,580
of 9,274,545 outputs
Outputs from BMC Bioinformatics
#3,630
of 3,932 outputs
Outputs of similar age
#219,664
of 260,860 outputs
Outputs of similar age from BMC Bioinformatics
#88
of 95 outputs
Altmetric has tracked 9,274,545 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,932 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.