The method exploits information from the reference alignments to classify the sequences in each alignment into a number of subfamilies and to construct a representative model for each protein subfamily, including characteristic conserved blocks and typical start/stop sites.
The best existing method exploits information from sequence and structure to achieve a precision (the fraction of predicted catalytic residues that are catalytic) of 18.5% at a corresponding recall (the fraction of catalytic residues identified) of 57% on a standard benchmark.
In the proposed algorithm, a genetic search algorithm is used as a global search method to explore the search space as much as possible, and a modified simulated annealing search algorithm is used as a local search method to exploit information in the search region.
Contrary to stereo-vision methods, SfS algorithms exploit information stored in pixel intensities in a single image.
These methods attempt to exploit information provided by intensity variation (gradient differences between the foreground lesion and the background) for the segmentation task.
Hence, state-of-the-art methods exploit multimodal information of users-item interactions to reduce sparsity, but they ignore preference dynamics and do not capture users' most recent preferences.
These methods exploit biological information including amino acid sequence [ 2– 9], genomic context [ 10– 14], protein interaction networks [ 15– 17], protein structure [ 18– 23], microarray [ 24], and literate to predict protein functions [ 25, 26].
However, here, we are not interested in image denoising methods that do not exploit information on noise type and statistics in their operation.
For this purpose, we developed a method fully exploiting information carried by the network (see Methods) and built a 2D landscape using a self-organising algorithm [ 14].
Our method of exploiting information redundancy from associations among SNP markers provides an efficient and relatively inexpensive method of searching for the optimal or approximate optimal subset of SNPs in genetic association studies.
