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It ranks template structures and selects the most likely candidates and estimates the reliability of the obtained lowest energy model.
The ligands of the top ranked templates are used as template ligands for searching against compound library.
Similar as in FINDSITEfilt, the ligands of the top ranked templates are used as reference small molecules for ligand based virtual screening, where the appropriate template ligands are obtained from the DrugBank [48] and ChEMBL [49] databases.
To identify and rank templates, we used profile profile alignments, profile sequence alignments and a machine learning approach (Cheng and Baldi, 2006).
This is to say that for easy targets, MULTICOM-CMFR (or MULTICOM-RANK) used only PSI-BLAST (or PSI-BLAST and then hhsearch) to identify and rank templates according to E-value as in MULTICOM-CLUSTER.
To speed-up, our method first aligns a target to all the templates using only the local alignment potential and then ranks all the templates using both the local and global alignment potentials described in Section 2. After ranking, only the first-ranked templates are used to build a 3D model by MODELLER for each target.
To rank the templates of a query sequence, if the maximal Sn1 is greater than or equal to the maximal Sn2, they will be ranked by Sn1, and otherwise by Sn2.
CNFpred first generates target template alignments and then ranks the templates using a neural network, which predicts the quality (TM-score) of an alignment.
Experimental results on the 1000 × 6000 threading set and the CASP10 set indicate that the pairwise potential indeed improves template selection, as shown in Figures 6 and 7. On the 1000 × 6000 set, the average TM-score increases from 0.547 to 0.566 when the pairwise potential is used to rank the templates.
In the initialization period, the first stage initializes, without priori knowledge of signal power ranking, the template of the stronger source, which is canceled from the composite ECG signal prior to initialization of the weaker source's template in the second stage.
In retrospect, the combined application of three factors led to the success of the design approach: utilizing the modular construction of the interface, capitalizing on native rather than artificial templates, and ranking with an accurate atom atom contact surface scoring function.
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