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To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar position weight matrices, our new models can distinguish the specificities of individual factors.
Lastly, Markov models can distinguish between remission and recovery periods by using temporary states (i.e. 6 more health states).
Thus, the 60-miRNA signature shared among all mouse liver tumor models can distinguish human tumors associated with elevated AFP expression, a marker of a poorly differentiated state.
Both time-dependent and age-dependent models can distinguish the three mechanisms (nonfunctionalization, neofunctionalization, and subfunctionalization) with high probabilities when the sample size is large.
We hypothesized that, among people with epilepsy, dense surface models can distinguish between those who do or do not have pathogenic structural variants.
We show that for both yeast and human bHLH TFs, our regression models can distinguish the binding specificities of individual family members, although their PWMs (Fig. 1) are highly similar.
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According to [157], two delivery models can distinguished for multiple Clouds.
By this way, the global discriminative appearance model can distinguish different targets more effectively.
Load-displacement response as well as concrete crack patterns are accurately simulated, and the proposed model can distinguish between the behavior of uncoated and epoxy-coated deformed bars as well as simulate the impact on bond strength of confinement provided by transverse steel.
Hence, the hybrid model can distinguish the short and long networks with the identical absolute cost difference as well as the identical relative cost difference.
However, the gases adsorption adaptation to the Langmuir–Freundlich model can distinguish that multi-layer adsorptivity is very feasible [14, 15].
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