Sentence examples for noisy interaction from inspiring English sources

Nonetheless, the above arguments cannot rule out the possibility that the main function of MTBs was to identify the more reliable miRNA-target pairs from the noisy interaction network, and thus co-regulation effects between mRNAs in the same MTBs were still stronger than other mRNAs targeted by the same miRNAs according to the network.

Our comparison with the effects of noisy interaction data (see figure 2) suggests that sampling and noise affect network statistics in different ways and we have therefore concentrated on the sampling effects as noise has received considerable attention previously (see, for example, [ 28, 13, 29]).

For instance, the noisy interaction pairs from high throughput experiments in human could be used to build neighbor pairs for training SML model (e.g. embedding on output) very naturally and thus the method has significant potential for intra-species PPI predictions such as in human.

Here we attempt to elucidate human ING protein interactions based the recently published yeast interactome data [ 19], hoping to identify real interactions in the long tail of low probability noisy interactions detected.

Experimental results show that the proposed approach has a good performance in distinguishing true interacting protein pairs from noisy protein protein interaction data.

The observation that such link-based evolutionary signals prevail in the topology of an otherwise extremely noisy protein interaction network indicates a novel way to uncover protein interactions in any organism for which orthologs can be identified from sequence data.

The term of noisy synaptic interactions requires a more careful treatment.

In particular, the results of the SOPH approach with noisy input interactions suggests that a method based on analyzing an ensemble of parsimonious solutions can exceed the accuracy of methods based on maximum likelihood.

Hence, some smoothing of the ν it parameters is necessary to ensure that we do not overparameterize the model and that noisy small space time interaction parameters shrink toward zero.

Although we found significant differences in co-factor expression and PWM score distributions between the cell types (see Additional file 13: Table S1 and Methods), because of limited and noisy PWM and interaction data it is difficult to quantify the overall effect of co-factor differences.

Hence, assembly coordination provides a potential mechanism for stabilizing the sequential activity patterns generated by noisy neurons, as interactions entrain cells in the population into coherent activation patterns within each theta cycle.