scholarly journals Verification of systems biology research in the age of collaborative competition

2012 ◽  
Vol 6 (S6) ◽  
Author(s):  
Carine Poussin
2011 ◽  
Vol 29 (9) ◽  
pp. 811-815 ◽  
Author(s):  
Pablo Meyer ◽  
Leonidas G Alexopoulos ◽  
Thomas Bonk ◽  
Andrea Califano ◽  
Carolyn R Cho ◽  
...  

2021 ◽  
Vol 11 (13) ◽  
pp. 5999
Author(s):  
Diego A. Camacho-Hernández ◽  
Victor E. Nieto-Caballero ◽  
José E. León-Burguete ◽  
Julio A. Freyre-González

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Much of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical validation; but no score has been developed to quantify statistically the noise in an arranged vector posterior to a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, in order to assess this problem.


2012 ◽  
Vol 6 (1) ◽  
pp. 67 ◽  
Author(s):  
Sarp A Coskun ◽  
Xinjian Qi ◽  
Ali Cakmak ◽  
En Cheng ◽  
A Cicek ◽  
...  

Author(s):  
Florencio Pazos ◽  
David Guijas ◽  
Manuel J. Gomez ◽  
Almudena Trigo ◽  
Victor de Lorenzo ◽  
...  

2020 ◽  
Vol 17 (166) ◽  
pp. 20200013 ◽  
Author(s):  
Zoe Schofield ◽  
Gabriel N. Meloni ◽  
Peter Tran ◽  
Christian Zerfass ◽  
Giovanni Sena ◽  
...  

The last five decades of molecular and systems biology research have provided unprecedented insights into the molecular and genetic basis of many cellular processes. Despite these insights, however, it is arguable that there is still only limited predictive understanding of cell behaviours. In particular, the basis of heterogeneity in single-cell behaviour and the initiation of many different metabolic, transcriptional or mechanical responses to environmental stimuli remain largely unexplained. To go beyond the status quo , the understanding of cell behaviours emerging from molecular genetics must be complemented with physical and physiological ones, focusing on the intracellular and extracellular conditions within and around cells. Here, we argue that such a combination of genetics, physics and physiology can be grounded on a bioelectrical conceptualization of cells. We motivate the reasoning behind such a proposal and describe examples where a bioelectrical view has been shown to, or can, provide predictive biological understanding. In addition, we discuss how this view opens up novel ways to control cell behaviours by electrical and electrochemical means, setting the stage for the emergence of bioelectrical engineering.


2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Yuriy L. Orlov ◽  
Ralf Hofestädt ◽  
Ancha V. Baranova

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