Sentence examples for bioinformatic method based on from inspiring English sources

Recently, a bioinformatic method based on reaction molecular signatures was proposed to predict catalytic and substrate promiscuity [ 49].

We developed a bioinformatic method based on a support vector machine for the prediction of internal ribosome entry sites in fungi using the 5'-UTR sequences of 20 non-redundant fungal organisms.

Bioinformatic methods based on conservation, structural information and/or amino acid physico-chemical properties have been developed to estimate the likelihood that a given missense variant is detrimental [ 100, 101].

Our bioinformatics method based on gene expression data can only address aspects of tissue instability that are related to steady-state mRNA levels.

Currently, bioinformatics methods based on the high homology between miRNAs and target genes are considered one of the most efficient approaches used to verify target genes, and has been confirmed in quite a number of studies [ 40- 43].

Instead of only relying on correlations of gene expression with the associated MMSE and NFT measures, and by using modern bioinformatics methods based on information theory and combinatorial optimization, we uncovered a 1,372-probe 1,372-probesion sigeneurexpressionsignatureigh-consensus withatstablished markers of presentsion in aD.

Bioinformatics methods based on the selenoprotein gene assembly algorithm SelGenAmic were used to identify 178 selenoprotein genes from 6 representative species from specific stages of invertebrate evolution.

Therefore, advanced bioinformatics methods based on co-abundance have been developed to enable grouping of microbial genes into 741 large MetaGenomic Units (MGUs) representing as-yet uncultured bacterial species.

Recent bioinformatic developments include an analysis using a homology detection method based on transitive sequence similarity search, in which similarity was measured by pairwise HMM profile comparison [ 34].

In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sines in proteins.

Dendrograms were constructed using the neighbor-joining method based on Nei's unbiased genetic distances using the Populations 1.2.30 beta2 program (http://bioinformatics.org/populations/).org/populations/