Sentence examples for predictions to generate from inspiring English sources

In addition, more various methods could be applied in the field of protein protein interaction predictions to generate more comprehensive interactiome.

The MAKER-P annotation pipeline combined evidence-based alignments and ab initio predictions to generate 50,172 gene models, of which 15,653 are classified as high confidence.

Briefly, we employed BCL Align, an alignment program that accounts for sequence identity and similarity as well as secondary structure and transmembrane region predictions, to generate an alignment of PMP22 (NP_696997.1) with claudin-15 (NP_068365.1).

The YeastFunc method, from the Roth group, also incorporates biological relationships inferred from various large-scale datasets, as well as sequence-based predictions, to generate GO annotations in all three aspects (25).

To identify annotations that needed to be reviewed and updated, all literature-based annotations were compared with all InterPro-based computational predictions to generate pairs of literature-based and computational annotations when both types of annotation existed for a given gene and GO aspect (Function, Process or Component).

In order to construct complex interaction and regulatory networks, Sun et al. incorporate information from multiple sources, including protein-protein interaction databases, pathway databases, transcription factor target gene predictions and microRNA target gene predictions to generate regulatory networks from an input gene set [6].

Candidate STEPs alleles were systematically submitted in the sequence context of their transcript-intron to a series of online splicing prediction algorithms to generate an in silico prediction of splicing efficiency via recognition of the 3' and 5' splice sites (Figure 3).

The numerical model uses a Monte Carlo prediction method to generate probabilistic distributions of indoor pollutant concentrations.

Each vehicle node locally records its historical passing route and applies prediction scheme to generate its VMP.

It is a well-known problem of both ChIP-on-chip experiments and motif prediction analyses to generate a large number of false positives.

An ideal prediction method will produce an AUC score of 1, while random predictions are expected to generate an AUC score of 0.5.