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However, relatively few previous studies have examined to what extent ratings of executive functioning can be used to predict functional impairments such as academic achievement.
Since the RCGs could successfully be used to predict functional annotations we asked whether they could also be useful in finding new candidate disease genes.
Gene or domain fusion is one of several genome context methods which can be used to predict functional associations between pairs of proteins [1], [2].
Similarly any score above 50 can be used to predict functional relationship for fused gene pairs with a probability of more than 95%.
We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs.
Nucleotide and/or amino acid sequence similarities have been extensively used to predict functional relation between genes [ 1, 2].
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In combining protein interaction network, highly connected coding genes linked by a given lncRNA were subsequently used to predict functions of the lncRNA through functional enrichment.
GeneSV was used to predict effects (functional vs. non-functional) of 37 amino acid substitutions on the NS5 polymerase (RdRp) of dengue virus type 2 (DENV-2), 36 of which are not observed in any publicly available DENV-2 sequence.
The functional single nucleotide polymorphism database (http://compbio.cs.queensu.ca/F-SNP/) was used to predict the functional effects of SNPs, and functional significance (FS) scores were applied for ranking potential deleterious effects of SNPs [ 5].
The MTB functional network produced is then used to predict the functional class of proteins of unknown function, evaluated using leave-one-out cross validation.
SIFT algorithm57 was used to predict the functional impact of the missense mutations.
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