Sentence examples for binding classification from inspiring English sources

In this work, we use a computational approximation of distance cut-off to define binding classification instead.

We studied the algorithm performance for peptide binding classification and compared it with SVM for a collection of both human and mice alleles.

The sparse representation (SR) approach proposed in this paper for peptide binding classification relies on the natural selective nature of the solution of an ℓ1-minimization problem [ 8].

For the set of alleles studied in this work, we found the DPPS encoding scheme to be efficient in conjunction with the proposed methodology for peptide binding classification.

30 Most recent binding site classification methods now use a combination of both sequence data and structural properties and as such require more advanced classification methodologies such as Support Vector Machines, Bayesian Networks, Neural Networks, Hidden Markov Models or Conditional Random Fields.

Figure 1 Automatic ligand and binding site classification.

Although the applications are not limited to the above mentioned, in this report, the aim was to emphasise the strength of such large scale analysis method and to demonstrate the capacity to provide a PDB-wide map of topological similarity at very low computational expenses as well as the robustness of the exact implementation in binding pocket classification.

To verify the binding site classification (ie, half-site, direct, inverted or everted repeats), we used Consite [65] to scan each sequence with a classical half-site position frequency matrix, and correlated the scores obtained with the experimental evidence.

In an effort to improve target prediction, we have previously employed a more sophisticated supervised learning method in Saccharomyces cerevisiae which combines many types of genomic data to assist binding site classification [ 10- 12].

The binding specificity classification we report here is broadly similar to that generated by aligning the ETS-domain peptide sequences from multiple species using the MUST program (Laudet et al, 1999) but differs from that generated by ClustalW using just human sequences by Hollenhorst et al (2007) or by us (Supplementary Figures S4 and S5).

Hereby, binding site-based classification outnumbers sequence-based classifications since similar binding sites can also be found in more distant proteins.