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Supervised learning methods in modeling of CD4+ T cell heterogeneity

Overview of attention for article published in BioData Mining, September 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#15 of 232)
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

news
2 news outlets
twitter
10 tweeters
googleplus
1 Google+ user

Readers on

mendeley
32 Mendeley
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Title
Supervised learning methods in modeling of CD4+ T cell heterogeneity
Published in
BioData Mining, September 2015
DOI 10.1186/s13040-015-0060-6
Pubmed ID
Authors

Pinyi Lu, Vida Abedi, Yongguo Mei, Raquel Hontecillas, Stefan Hoops, Adria Carbo, Josep Bassaganya-Riera

Abstract

Modeling of the immune system - a highly non-linear and complex system - requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales. This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models. Our results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF. Using machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 19%
Student > Ph. D. Student 5 16%
Researcher 5 16%
Student > Doctoral Student 4 13%
Student > Master 3 9%
Other 7 22%
Unknown 2 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 22%
Agricultural and Biological Sciences 5 16%
Medicine and Dentistry 4 13%
Business, Management and Accounting 3 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Other 7 22%
Unknown 4 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 27 January 2017.
All research outputs
#654,004
of 13,041,733 outputs
Outputs from BioData Mining
#15
of 232 outputs
Outputs of similar age
#16,620
of 238,598 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 13,041,733 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 232 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has done particularly well, scoring higher than 93% of its peers.
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 238,598 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
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