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The NetPhos [ 22] algorithm was utilized to predict putative phosphorylation sites for both the wild type and the variant protein sequences.
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Several distinct algorithmic approaches are utilized to predict miRNA targets.
Numerous approaches have been utilized to predict and discover miRNAs [ 57, 58].
Protein physical interaction data is often utilized to predict protein function.
We have also demonstrated that molecular hypotheses can be utilized to predict drug response in vivo.
TMPRED [ 25] was utilized to predict the number and locations of transmembrane helices.
We utilized the NetPhos algorithm to predict putative phosphorylation sites along the DNA repair and cell cycle proteins, and studied whether 89 naturally occurring nsSNPs (64 from 28 DNA repair and 25 from 19 cell cycle genes) might alter the phosphorylation patterns in these proteins.
We also use the TRAP software to predict putative binding factors.
Target-align [ 29], a miRNA target prediction tool, was used to predict putative miRNA target genes.
We used Srnaloop to predict putative miRNAs from the silkworm genome.
Public libraries were queried to predict putative miRNA targets.
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