Information on computationally inferred functional TF binding sites and non-functional ones for 37 yeast TFs at normal condition was taken from publication by Gao et al. [ 8].
When studying the above SNPs with F-SNP-program that is freely available on the internet (http://compbio.cs.queensu.ca/F-SNP/) connected to the main databases, and computationally predicting functional SNPs, all four SNPs are predicted to be functional as they seem to be in the transcription factor binding site [ 31].
Rather than calculating an exact nonlinearity measure which is often computationally intensive, functional expansion (FEx) models are used to derive approximate lower and upper bounds on the measure.
This information, along with protein structures, when available, can be used to computationally predict functional regions of proteins.
Because the Ciona intestinalis genome is incompletely annotated, we computationally assigned functional descriptions to the sequences.
Röthlisberger et al. have computationally designed functional Kemp elimination catalysts using R osettaM atch.
Here, we introduce SNPdbe SNP database of effects, with predictions of computationally annotated functional impacts of SNPs.
Here, the computationally predicted functional role for Stat3 in primitive, but not definitive, erythroid cell maturation is validated in vitro.
Lower costs in genomic sequencing and improved methods of generating computationally predicted functional annotations has led to the development of many model organism databases using the BioCyc framework [ 1].
A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper.
