Finally, a common drawback of most existing sequence-based B-cell epitope prediction methods is that they are residues-state prediction; that is, they can only simply predict the antigenic determinant residues from sequences but cannot tell which residues may potentially form the real epitopes.
The magnetic resonance condition can be simply predicted by a modified capacitor inductor (LC) model, and electromagnetic field distributions are presented to illustrate the unique behavior of magnetic polaritons such as field localization and induced currents.
Without a reference genome, the total number of restriction sites can be simply predicted based on genome size and GC content by assuming sites that are Poisson distributed.
In addition, k2,DEAB can also be simply predicted using a predictive correlation developed based on the Brφnsted plot with an AAD of 7.6%.
The SKM is based on the RANS equations to provide quasi-2D model by which the lateral distributions of depth-averaged velocity and boundary shear stress across rivers and channels can simply be predicted.
The freeze-thaw damage of rock is expressed by static elastic modulus in this paper, which can be accurately and simply predicted by damage evolution equation using P-wave velocity or compression strength proposed by Liu et al., 2015.
For example, one of our observations is that, according to many commonly used metrics, a sizeable fraction of performance can be explained by simply predicting high node degree genes often (because high node degree genes are often multifunctional).
The confidence of the fitting surface roughness results was 97% by BP network model and the average error was 5.8%, which can simply and successfully predict surface roughness during sanding process.
For example, if a given dataset has a class distribution of 98 2, as is fairly common in various real-world scenarios, (a common example is medical diagnosis [21, 22]) then one can be 98% accurate by simply predicting all examples as majority class.
Using gene contents for selection candidates, i.e. individuals whose genetic merit is predicted using SNP effects estimated from the training data, is not inhibited by the proposed algorithms, because their predicted genetic merit can simply be obtained outside the algorithm by multiplying their gene contents with allele substitution effects that are estimated in the algorithm.
