ROC curves and AUC scores to compare the classifier performance of the different data sets containing various number of features.

ROC curves and AUC scores to compare the classifier performance of the different data sets containing various number of features.

<p>In total 14 features related to hyperbolic properties and centrality measures were used to predict phosphorylation and dephosphorylation directed PPIs.</p> <p>(JPG)</p>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Aimilia-Christina Vagiona (17584758) (author)
مؤلفون آخرون: Sofia Notopoulou (14256494) (author), Zbyněk Zdráhal (3324678) (author), Mariane Gonçalves-Kulik (20818278) (author), Spyros Petrakis (143222) (author), Miguel A. Andrade-Navarro (31443) (author)
منشور في: 2025
الموضوعات:
Biophysics
Biochemistry
Genetics
Molecular Biology
Cancer
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
would allow us
regulates many functions
mapping contains information
compact cluster composed
cellular model revealed
hyperbolic geometry underlying
specifically interactions related
random forest algorithm
div >< p
related directed ppis
related disease network
related ppis
disease pathology
network representation
interaction network
hyperbolic space
hyperbolic properties
protein interactions
translational modification
recent years
ptm ).
proteomics analysis
predicted ptm
indeed dysregulated
increasing accumulation
explore whether
evidence supporting
dysregulated ptm
complex systems
centrality measures
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الوصف
الملخص:<p>In total 14 features related to hyperbolic properties and centrality measures were used to predict phosphorylation and dephosphorylation directed PPIs.</p> <p>(JPG)</p>

مواد مشابهة