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However, these methods usually do not focus on comparison of several activity classes.
The statistical methods usually do not consider such context and this is one of the reasons why it is advised to first identify the linguistic properties of the candidates and, then, apply statistical methods.
Such approaches as the vertex-based methods usually do not further select the active interaction relationships among the identified proteins.
For instance, neural network methods usually do not take the interdependency relationship among the labels of adjacent residues into consideration.
These single marker−based methods usually do not consider the effects of (both genotyped and ungenotyped) neighboring markers and hence may fail to discover QTL for complex traits.
These studies have discussed the issue in a qualitative manner, but without putting the recreational value on the same monetary scale as traditional engineering methods usually do.
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The active measurement method usually does not require collaboration among multiple nodes, which is very flexible.
These are pretty stringent criteria, and the method usually doesn't turn up very many stocks with decent liquidity.
For other cases of σ 1 and σ 2 that are not covered by Theorem 1 and Theorem 2, the above method usually does not give sufficient conditions for the oscillation of all solutions of Eq. (1).
This approach has found wide applications, although it cannot guarantee a full account of stochasticity, as this method usually does not generate an exhaustive number of trajectories that cover all possible locations in the probability landscape.
This method usually does more harm than good, so think very carefully before trialing it.
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