Sentence examples for bioinformatic algorithms have been from inspiring English sources

Bioinformatic algorithms have been developed to use SNP array information to identify genomic aberrations such as DNA copy number changes and loss-of heterozygosity (loss-of heterozygosityf DNA with excLOHively homozygous markers [1]–[8].e

A variety of bioinformatic algorithms have been developed which predict potential binding sites within the 3'-untranslated regions (UTRs) of mRNAs [ 51].

A significant number of bioinformatics algorithms have been developed to detect fusion genes (Beccuti et al., 2013).

Based on the importance of seed pairing, multiple bioinformatics algorithms have been developed in order to predict miRNA-binding sites in mRNA sequences (4– 8).

To assist with these challenges, bioinformatics algorithms have been developed that can predict the secretory type of proteins from primary sequence based on signal peptide pattern, transmembrane domain or other motifs.

Bioinformatics' strategies and miRNA gene prediction algorithms have been used to screen genome sequences and to identify potential miRNAs [[ 2], for review [ 12]].

Machine learning and data mining algorithms have been widely used in bioinformatics and computational biology [ 24– 28].

As the subsets used for different biclusters of genes are not known beforehand, several biclustering algorithms have been proposed in the bioinformatics literature to identify them [ 5- 13].

Machine learning algorithms have been successfully applied to many bioinformatics problems.

RF algorithms have been used widely in data mining, bioinformatics, management, economics, and the medical sciences (Fang and Jian-Bina 2011).

The exploitation of computational methods for data mining and machine learning as well as bioinformatics methods for incorporating prior biological knowledge into data analysis algorithms has been proved to be an effective strategy.