Actually, using the FunD information to formulate protein samples for statistical predictions was originally proposed in [51], [52], and quite encouraged results were achieved.
For the simulation of dynamic behavior of MSMPR crystallizer in various technological conditions an artificial neural network specialized in time series prediction was originally used.
Pitch prediction (PLP), also known as long-term prediction, was originally proposed for speech modeling and coding, and was more recently applied to audio signal modeling in the context of the MPEG-4 advanced audio coder (AAC) [38, 39].
This prediction was originally tested in 100 organisms [ 1] and a similar analysis expanded the dataset to over 14,000 organisms [ 3].
The original CFM positive and negative models were used for the spectrum prediction, which were originally trained on data from the METLIN database.
These prediction tools were originally designed to forecast the functional impact of an nsSNV in disease domains.
Geostatistical prediction techniques were originally developed for, and remain principally targeted at, spatial-only settings (Chilès and Delfiner, 1999; Goovaerts, 1997; Matheron, 1971).
Second, the disease-gene prediction tools [20], [21], [23] were originally developed for linkage analysis and often neglected genomic features of SNPs.
This method [ 13] is one of a family of Bayesian methods that were originally conceived for prediction of genetic merit and phenotypes and later extended to map gene locations [ 6, 26].
MutationTaster scores and predictions were also extracted from dbNSFP, which were originally queried from the website of MutationTaster.
