To compare the prediction accuracy of our SVR-based method against a naive supervised learning approach, we implemented a prediction framework based on the nearest neighbor (NN) algorithm.
Measures such as the c-statistic and the measures of reclassification do not capture the consequences of implementing a prediction model.
Secondly, we implemented a prediction of protein sequences based on unigene sequences using ESTScan software http://estscan.sourceforge.net/[ 14] and an SGN Perl script (longest6frame.pl), which simply determines the longest open reading frame and translates it into a protein sequence.
Furthermore, we implement a simple prediction method, where we set p a,s)(t) = p a,s)(t c ) and u s (t) = u s (t c ), i.e., we assume that using the present network state will remain unchanged during whole prediction window.
A three-step process is applicable in order to implement a subsidence-prediction model in the GIS environment.
Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation.
In the end, we implement a planning-based prediction model for a real-world company for expansion of the business interests.
A study is conducted with the aim of developing and implementing a fatigue prediction model for assessing the damage tolerance capabilities of polymer matrix composite structures.
For target sequence prediction, we implemented a combined target prediction using three common prediction algorithms, miRanda, RNAhybrid and SeedMatch.
The downstream-sense/upstream antisense Arabidopsis whole genome tiling expression data for validated miRNA target genes versus non-target paralogs was adopted as a key expression feature, along with smRNA abundances and thermodynamic energy of binding to implement an SVM for prediction of miRNA targets and genes.
