As the number of determined structures of membrane proteins increases so does the need for computational methods which predict their position in the lipid bilayer.
In sum, we have created comprehensive benchmarks for methods which predict the location of transcription factor binding sites and have used the benchmark to evaluate the effects of using different motif representations and of using comparative genomics in predictions.
There are some structure-based methods, which predict a protein's position within the membrane.
However, most of above methods are unsupervised learning methods, which predict the protein complexes based on the pre-defined rules.
Methods, which predict the localization at the end of a complex pathway, are less reliable when applied directly to sequences.
To our knowledge, there are no methods which predict drug target interactions based on chemical, genomic and pharmacological data simultaneously.
This approach was taken with the implicit hope that methods which predict drug classes well will also do better at predicting disease indications for drugs.
GEBV accuracies predicted in this way are the same as those from BLUP methods, which predict individual SNP effects since the models are equivalent [ 2, 3, 12, 13].
If only the amino acid sequence is available, the three-dimensional structure may be inferred either from methods based on homology, such as threading, or from ab initio methods, which predict the structure from the sequence alone [ 44].
This paper extends a previous derivation of a design method, which predicted the membrane load-carrying capacity of composite floor slabs in fire, but was limited to isotropic reinforcement.
