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The most common way of amino acid encoding is the binary encoding scheme represented by a 20-bit binary vector, where 19 bits are set to zero and one bit is set to 1. Property encoding, on the other hand, is based on a vector containing one or more amino acid properties.
The algorithm is designed based on a vector space theory with correctness proof.
We propose a method based on a vector field distance transform of the mesh to process.
IPM is based on a vector model while LSA involves an ideal point model.
This scheme is based on a vector radiative transfer model of RT3 of Evans and Stephens (1991) with two modifications.
In this paper, we propose a novel relaxed lightweight assembly retrieval approach based on a vector space model (VSM).
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Our targeting system is based on Ad vector targeting via scFv-mediated binding to a cell surface molecule.
The second one is based on a vector-valued lower-unbounded Zhang function and is termed ZNN-2 model.
The first ZNN model is based on a vector-valued lower-bounded Zhang function and is termed ZNN-1 model.
The technique is based on a vector-potential formulation which offers some computational advantages over other methods based on the Biot Savart integral.
Figure 20 shows the developed fragility surface based on a vector-valued IM and can be visualized as fragility curves by projecting the surface onto the planes.
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