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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.
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.
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An interesting application of our framework is enabling an entity to maintain an encrypted database on third party hardware with a similar structure of Netflix's dataset and being able to implement a prediction algorithm with minimum data leakage to the DBMS.
Measures such as the c-statistic and the measures of reclassification do not capture the consequences of implementing a prediction model.
For target sequence prediction, we implemented a combined target prediction using three common prediction algorithms, miRanda, RNAhybrid and SeedMatch.
We implemented a two-stage prediction process.
We have designed and implemented a protein structure prediction pipeline using homology modelling based on Martí-Renom methodologies [ 10].
We have implemented a table of predictions of gene-disease relationships extracted from the PhenoPred tool developed at Indiana University [29].
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.
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 recent publication by Oates and colleagues (DP: database of disordered protein predictions) [ 67] included predictions for several trypanosomatid genomes and implemented a distinctive approach to estimating prediction agreement among the evaluated algorithms.
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