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Multilevel modeling analyses that include a measurement level can estimate the occasionlevel (e.g., days or beeps or interactions) reliability of scales.
For an increased number of failed critical configurations and configuration interactions reliability and performance of software system deteriorate significantly.
In order to get an estimate of an interactions reliability, we employed a logistic regression model.
For physical interactions, reliability also varies considerably depending on the size of the experiment and the method used to identify them.
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The alternating decision tree algorithm is used both for identifying discriminating biological features that could be used for assessing protein interaction reliability and for constructing a classifier to identify true positive interacting pairs.
Secondly, we use a novel negative example generation method for assessing protein interaction reliability.
To learn a prediction model for assessing protein interaction reliability, we first prepared training interaction pairs which are labeled with a target class, as described in Sect.
Deng et al. used a maximum likelihood estimation method to assess protein interaction reliability using genomic features of gene expression correlation and protein function.
As a result, we constructed an evaluation system for assessing protein interaction reliability using positive datasets obtained from known protein complex and negative datasets derived based on co-localization characteristics.
Rules IV and VI: Since marginal essentiality and absolute mRNA expression produce low confidential predictions, they could be regarded as relatively weak evidences for assessing protein protein interaction reliability.
Edge colors indicate interaction reliability with darker edges denoting higher scores and greater reliability.
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